human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "greed is good near optimal submodular maximization via greedy optimization"
  Abstract: It is known that greedy methods perform well for maximizing monotone submodular functions. At the same time, such methods perform poorly in the face of non-monotonicity. In this paper, we show - arguably, surprisingly - that invoking the classical greedy algorithm $O(\sqrt{k})$-times leads to the (currently) fastest deterministic algorithm, called Repeated Greedy, for maximizing a general submodular function subject to $k$-independent system constraints. Repeated Greedy achieves $(1 + O(1/\sqrt{k}))k$ approximation using $O(nr\sqrt{k})$ function evaluations (here, $n$ and $r$ denote the size of the ground set and the maximum size of a feasible solution, respectively). We then show that by a careful sampling procedure, we can run the greedy algorithm only once and obtain the (currently) fastest randomized algorithm, called Sample Greedy, for maximizing a submodular function subject to $k$-extendible system constraints (a subclass of $k$-independent system constrains). Sample Greedy achieves $(k + 3)$-approximation with only $O(nr/k)$ function evaluations. Finally, we derive an almost matching lower bound, and show that no polynomial time algorithm can have an approximation ratio smaller than $ k + 1/2 - \varepsilon$. To further support our theoretical results, we compare the performance of Repeated Greedy and Sample Greedy with prior art in a concrete application (movie recommendation). We consistently observe that while Sample Greedy achieves practically the same utility as the best baseline, it performs at least two orders of magnitude faster.

Connected through hyperedge HE-1 (induced by source paper "regularized submodular maximization at scale"):
  with members:
    - "flexible modeling of diversity with strongly log concave distributions"
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "strong log concavity does not imply log submodularity"
    - "an optimal streaming algorithm for non monotone submodular maximization"
    - "making a sieve random improved semi streaming algorithm for submodular maximization under a cardinality constraint"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "data summarization at scale a two stage submodular approach"
    - "a tight approximation for submodular maximization with mixed packing and covering constraints"

Connected through hyperedge HE-2 (induced by source paper "optimal streaming algorithms for submodular maximization with cardinality constraints"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "constrained submodular maximization beyond 1 e"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized composable core sets for distributed submodular maximization"

Connected through hyperedge HE-3 (induced by source paper "streaming submodular maximization under a k set system constraint"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "an optimal streaming algorithm for non monotone submodular maximization"
    - "making a sieve random improved semi streaming algorithm for submodular maximization under a cardinality constraint"
    - "data summarization at scale a two stage submodular approach"
    - "do less get more streaming submodular maximization with subsampling"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "discrete adversarial attacks and submodular optimization with applications to text classification"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"

Connected through hyperedge HE-4 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online"):
  with members:
    - "a nearly linear time algorithm for submodular maximization with a knapsack constraint"
    - "on budget feasible mechanism design for symmetric submodular objectives"
    - "simple and efficient budget feasible mechanisms for monotone submodular valuations"
    - "budget feasible mechanisms on matroids"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "coverage matching and beyond new results on budgeted mechanism design"
    - "submodular secretary problems cardinality matching and linear constraints"
    - "bayesian budget feasibility with posted pricing"

Connected through hyperedge HE-5 (induced by source paper "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "data summarization at scale a two stage submodular approach"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "parallelizing greedy for submodular set function maximization in matroids and beyond"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"

Connected through hyperedge HE-6 (induced by source paper "guarantees of stochastic greedy algorithms for non monotone submodular maximization with cardinality constraint"):
  with members:
    - "interlaced greedy algorithm for maximization of submodular functions in nearly linear time"
    - "a memoization framework for scaling submodular optimization to large scale problems"
    - "non monotone submodular maximization with nearly optimal adaptivity and query complexity"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "stochastic greedy closing the optimality gap in exact weak submodular maximization"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"

Connected through hyperedge HE-7 (induced by source paper "interlaced greedy algorithm for maximization of submodular functions in nearly linear time"):
  with members:
    - "parallel algorithm for non monotone dr submodular maximization"
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "constrained submodular maximization via a non symmetric technique"
    - "constrained submodular maximization beyond 1 e"

Connected through hyperedge HE-8 (induced by source paper "an optimal monotone contention resolution scheme for bipartite matchings via a polyhedral viewpoint"):
  with members:
    - "understanding the correlation gap for matchings"
    - "submodular maximization through the lens of linear programming"
    - "constrained submodular maximization beyond 1 e"
    - "online contention resolution schemes"
    - "non negative submodular stochastic probing via stochastic contention resolution schemes"
    - "a stochastic probing problem with applications"
    - "a k 3 2 approximation algorithm for monotone submodular k set packing and general k exchange systems"
    - "symmetry and approximability of submodular maximization problems"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.LG(Machine Learning)