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: "symmetry and approximability of submodular maximization problems"
  Abstract: A number of recent results on optimization problems involving submodular functions have made use of the multilinear relaxation of the problem. These results hold typically in the value oracle model, where the objective function is accessible via a black box returning f(S) for a given S. We present a general approach to deriving inapproximability results in the value oracle model, based on the notion of symmetry gap. Our main result is that for any fixed instance that exhibits a certain symmetry gap in its multilinear relaxation, there is a naturally related class of instances for which a better approximation factor than the symmetry gap would require exponentially many oracle queries. This unifies several known hardness results for submodular maximization, and implies several new ones. In particular, we prove that there is no constant-factor approximation for the problem of maximizing a non-negative submodular function over the bases of a matroid. We also provide a closely matching approximation algorithm for this problem.

Connected through hyperedge HE-1 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "multi pass streaming algorithms for monotone submodular function maximization"
    - "towards nearly linear time algorithms for submodular maximization with a matroid constraint"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "a nearly linear time algorithm for submodular maximization with a knapsack constraint"

Connected through hyperedge HE-2 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "the fast algorithm for submodular maximization"
    - "non monotone submodular maximization with nearly optimal adaptivity and query complexity"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular optimization in the mapreduce model"
    - "unconstrained submodular maximization with constant adaptive complexity"

Connected through hyperedge HE-3 (induced by source paper "online non monotone dr submodular maximization"):
  with members:
    - "introduction to online convex optimization"
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "an optimal algorithm for online unconstrained submodular maximization"
    - "non monotone continuous dr submodular maximization structure and algorithms"
    - "gradient methods for submodular maximization"
    - "robust budget allocation via continuous submodular functions"
    - "potential function proofs for first order methods"
    - "guaranteed non convex optimization submodular maximization over continuous domains"

Connected through hyperedge HE-4 (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-5 (induced by source paper "non monotone dr submodular maximization approximation and regret guarantees"):
  with members:
    - "introduction to online convex optimization"
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "an optimal algorithm for online unconstrained submodular maximization"
    - "non monotone continuous dr submodular maximization structure and algorithms"
    - "gradient methods for submodular maximization"
    - "robust budget allocation via continuous submodular functions"
    - "non monotone dr submodular function maximization"
    - "guaranteed non convex optimization submodular maximization over continuous domains"

Connected through hyperedge HE-6 (induced by source paper "making a sieve random improved semi streaming algorithm for submodular maximization under a cardinality constraint"):
  with members:
    - "deterministic 1 2 epsilon approximation for submodular maximization over a matroid"
    - "online submodular maximization beating 1 2 made simple"
    - "do less get more streaming submodular maximization with subsampling"
    - "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"
    - "streaming algorithms for submodular function maximization"
    - "online submodular maximization with preemption"

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"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "constrained submodular maximization via a non symmetric technique"

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"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "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"

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.DS(Data Structures and Algorithms)