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: "smoothing method for approximate extensive form perfect equilibrium"
  Abstract: Nash equilibrium is a popular solution concept for solving imperfect-information games in practice. However, it has a major drawback: it does not preclude suboptimal play in branches of the game tree that are not reached in equilibrium. Equilibrium refinements can mend this issue, but have experienced little practical adoption. This is largely due to a lack of scalable algorithms. #R##N#Sparse iterative methods, in particular first-order methods, are known to be among the most effective algorithms for computing Nash equilibria in large-scale two-player zero-sum extensive-form games. In this paper, we provide, to our knowledge, the first extension of these methods to equilibrium refinements. We develop a smoothing approach for behavioral perturbations of the convex polytope that encompasses the strategy spaces of players in an extensive-form game. This enables one to compute an approximate variant of extensive-form perfect equilibria. Experiments show that our smoothing approach leads to solutions with dramatically stronger strategies at information sets that are reached with low probability in approximate Nash equilibria, while retaining the overall convergence rate associated with fast algorithms for Nash equilibrium. This has benefits both in approximate equilibrium finding (such approximation is necessary in practice in large games) where some probabilities are low while possibly heading toward zero in the limit, and exact equilibrium computation where the low probabilities are actually zero.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for green security games with real time information"):
  with members:
    - "safe and nested subgame solving for imperfect information games"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "asynchronous methods for deep reinforcement learning"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "action conditional video prediction using deep networks in atari games"
    - "dueling network architectures for deep reinforcement learning"
    - "deep recurrent q learning for partially observable mdps"

Connected through hyperedge HE-2 (induced by source paper "regret circuits composability of regret minimizers"):
  with members:
    - "solving imperfect information games via discounted regret minimization"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "solving large extensive form games with strategy constraints"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "safe and nested subgame solving for imperfect information games"
    - "regret minimization in behaviorally constrained zero sum games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "extensive form perfect equilibrium computation in two player games"

Connected through hyperedge HE-3 (induced by source paper "online convex optimization for sequential decision processes and extensive form games"):
  with members:
    - "introduction to online convex optimization"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "regret minimization for partially observable deep reinforcement learning"
    - "theoretical and practical advances on smoothing for extensive form games"
    - "regret minimization in behaviorally constrained zero sum games"
    - "extensive form perfect equilibrium computation in two player games"
    - "imperfect recall abstractions with bounds in games"
    - "no regret learning in extensive form games with imperfect recall"

Connected through hyperedge HE-4 (induced by source paper "quasi perfect stackelberg equilibrium"):
  with members:
    - "robust stackelberg equilibria in extensive form games and extension to limited lookahead"
    - "regret minimization in behaviorally constrained zero sum games"
    - "extensive form perfect equilibrium computation in two player games"

Connected through hyperedge HE-5 (induced by source paper "solving large sequential games with the excessive gap technique"):
  with members:
    - "theoretical and practical advances on smoothing for extensive form games"
    - "measuring the size of large no limit poker games"

Connected through hyperedge HE-6 (induced by source paper "regret minimization in behaviorally constrained zero sum games"):
  with members:
    - "theoretical and practical advances on smoothing for extensive form games"
    - "extensive form perfect equilibrium computation in two player games"
    - "imperfect recall abstractions with bounds in games"
    - "no regret learning in extensive form games with imperfect recall"
    - "bayes bluff opponent modelling in poker"
    - "blackwell approachability and minimax theory"

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.GT(Computer Science and Game Theory)