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: "a tight combinatorial algorithm for submodular maximization subject to a matroid constraint"
  Abstract: We present an optimal, combinatorial 1-1/e approximation algorithm for monotone submodular optimization over a matroid constraint. Compared to the continuous greedy algorithm (Calinescu, Chekuri, Pal and Vondrak, 2008), our algorithm is extremely simple and requires no rounding. It consists of the greedy algorithm followed by local search. Both phases are run not on the actual objective function, but on a related non-oblivious potential function, which is also monotone submodular. Our algorithm runs in randomized time O(n^8u), where n is the rank of the given matroid and u is the size of its ground set. We additionally obtain a 1-1/e-eps approximation algorithm running in randomized time O (eps^-3n^4u). For matroids in which n = o(u), this improves on the runtime of the continuous greedy algorithm. The improvement is due primarily to the time required by the pipage rounding phase, which we avoid altogether. Furthermore, the independence of our algorithm from pipage rounding techniques suggests that our general approach may be helpful in contexts such as monotone submodular maximization subject to multiple matroid constraints. #R##N#Our approach generalizes to the case where the monotone submodular function has restricted curvature. For any curvature c, we adapt our algorithm to produce a (1-e^-c)/c approximation. This result complements results of Vondrak (2008), who has shown that the continuous greedy algorithm produces a (1-e^-c)/c approximation when the objective function has curvature c. He has also proved that achieving any better approximation ratio is impossible in the value oracle model.

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 "non submodular function maximization subject to a matroid constraint with applications"):
  with members:
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "steering social activity a stochastic optimal control point of view"
    - "equity of attention amortizing individual fairness in rankings"
    - "deep reinforcement learning of marked temporal point processes"
    - "fairness of exposure in rankings"
    - "combinatorial penalties which structures are preserved by convex relaxations"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "gradient methods for submodular maximization"

Connected through hyperedge HE-3 (induced by source paper "multi pass streaming algorithms for monotone submodular function maximization"):
  with members:
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "almost optimal streaming algorithms for coverage problems"
    - "maximizing a monotone submodular function with a bounded curvature under a knapsack constraint"
    - "streaming algorithms for submodular function maximization"
    - "lazier than lazy greedy"
    - "submodular maximization meets streaming matchings matroids and more"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"

Connected through hyperedge HE-4 (induced by source paper "randomized strategies for robust combinatorial optimization"):
  with members:
    - "robust monotone submodular function maximization"
    - "robust optimization for non convex objectives"
    - "robust randomized matchings"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"

Connected through hyperedge HE-5 (induced by source paper "constrained submodular maximization via greedy local search"):
  with members:
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "robust and maxmin optimization under matroid and knapsack uncertainty sets"
    - "thresholded covering algorithms for robust and max min optimization"

Connected through hyperedge HE-6 (induced by source paper "learning based compressive subsampling"):
  with members:
    - "a totally unimodular view of structured sparsity"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "group sparse model selection hardness and relaxations"
    - "an algorithm for variable density sampling with block constrained acquisition"
    - "energy aware adaptive bi lipschitz embeddings"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "on variable density compressive sampling"
    - "model based compressive sensing"

Connected through hyperedge HE-7 (induced by source paper "on maximizing a monotone k submodular function subject to a matroid constraint"):
  with members:
    - "maximizing k submodular functions and beyond"
    - "improved approximation algorithms for k submodular function maximization"
    - "towards minimizing k submodular functions"
    - "potts model parametric maxflow and k submodular functions"
    - "adaptive submodular optimization under matroid constraints"

Connected through hyperedge HE-8 (induced by source paper "string submodular functions with curvature constraints"):
  with members:
    - "communications inspired projection design with application to compressive sensing"
    - "submodularity and optimality of fusion rules in balanced binary relay trees"
    - "maximizing sequence submodular functions and its application to online advertising"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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)