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: "simple strategies for large zero sum games with applications to complexity theory"
  Abstract: Von Neumann's Min-Max Theorem guarantees that each player of a zero-sum matrix game has an optimal mixed strategy. This paper gives an elementary proof that each player has a near-optimal mixed strategy that chooses uniformly at random from a multiset of pure strategies of size logarithmic in the number of pure strategies available to the opponent. #R##N#For exponentially large games, for which even representing an optimal mixed strategy can require exponential space, it follows that there are near-optimal, linear-size strategies. These strategies are easy to play and serve as small witnesses to the approximate value of the game. #R##N#As a corollary, it follows that every language has small ``hard'' multisets of inputs certifying that small circuits can't decide the language. For example, if SAT does not have polynomial-size circuits, then, for each n and c, there is a set of n^(O(c)) Boolean formulae of size n such that no circuit of size n^c (or algorithm running in time n^c) classifies more than two-thirds of the formulae succesfully.

Connected through hyperedge HE-1 (induced by source paper "complexity theory game theory and economics the barbados lectures"):
  with members:
    - "a round collapse theorem for computationally sound protocols or tfnp is hard on average in pessiland"
    - "on communication complexity of fixed point computation"
    - "near optimal communication lower bounds for approximate nash equilibria"
    - "query to communication lifting for bpp"
    - "lifting randomized query complexity to randomized communication complexity"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "communication complexity of approximate nash equilibria"
    - "the price of anarchy in auctions"

Connected through hyperedge HE-2 (induced by source paper "complexity limitations on one turn quantum refereed games"):
  with members:
    - "quantum interactive proofs with competing provers"
    - "quantum arthur merlin games"
    - "playing games with algorithms algorithmic combinatorial game theory"
    - "complexity limitations on quantum computation"

Connected through hyperedge HE-3 (induced by source paper "generalization and equilibrium in generative adversarial nets gans"):
  with members:
    - "learning to protect communications with adversarial neural cryptography"
    - "do gans actually learn the distribution an empirical study"
    - "nips 2016 tutorial generative adversarial networks"
    - "stacked generative adversarial networks"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "sparse sums of positive semidefinite matrices"):
  with members:
    - "a matrix hyperbolic cosine algorithm and applications"
    - "faster spectral sparsification and numerical algorithms for sdd matrices"
    - "a parallel approximation algorithm for positive semidefinite programming"
    - "near optimal column based matrix reconstruction"
    - "graph sparsification by edge connectivity and random spanning trees"
    - "towards an sdp based approach to spectral methods a nearly linear time algorithm for graph partitioning and decomposition"
    - "a linear time algorithm for sparsification of unweighted graphs"
    - "graph sparsification via refinement sampling"

Connected through hyperedge HE-5 (induced by source paper "solving combinatorial games using products projections and lexicographically optimal bases"):
  with members:
    - "the computational power of optimization in online learning"
    - "second order quantile methods for experts and combinatorial games"
    - "importance weighting without importance weights an efficient algorithm for combinatorial semi bandits"
    - "optimization learning and games with predictable sequences"
    - "the matching polytope has exponential extension complexity"
    - "entropy optimization and counting"
    - "regret in online combinatorial optimization"
    - "on the universality of online mirror descent"

Connected through hyperedge HE-6 (induced by source paper "the query complexity of correlated equilibria"):
  with members:
    - "well supported versus approximate nash equilibria query complexity of large games"
    - "learning equilibria of games via payoff queries"
    - "empirical distribution of equilibrium play and its testing application"
    - "query complexity of approximate nash equilibria"
    - "query complexity of correlated equilibrium"
    - "polynomial time computation of exact correlated equilibrium in compact games"

Connected through hyperedge HE-7 (induced by source paper "conspiracies between learning algorithms circuit lower bounds and pseudorandomness"):
  with members:
    - "graph isomorphism and circuit size"
    - "analysis of boolean functions"
    - "new algorithms and lower bounds for circuits with linear threshold gates"
    - "natural proofs versus derandomization"

Connected through hyperedge HE-8 (induced by source paper "team maxmin equilibrium efficiency bounds and algorithms"):
  with members:
    - "on the value of correlation"
    - "approximability and parameterized complexity of minmax values"

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.CC(Computational Complexity)