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: "openspiel a framework for reinforcement learning in games"
  Abstract: OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games, as well as traditional multiagent environments such as (partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel also includes tools to analyze learning dynamics and other common evaluation metrics. This document serves both as an overview of the code base and an introduction to the terminology, core concepts, and algorithms across the fields of reinforcement learning, computational game theory, and search.

Connected through hyperedge HE-1 (induced by source paper "from poincar e recurrence to convergence in imperfect information games finding equilibrium via regularization"):
  with members:
    - "from darwin to poincar e and von neumann recurrence and cycles in evolutionary and algorithmic game theory"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "a theory of regularized markov decision processes"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "the mechanics of n player differentiable games"
    - "stable opponent shaping in differentiable games"
    - "modeling friends and foes"
    - "negative momentum for improved game dynamics"

Connected through hyperedge HE-2 (induced by source paper "accelerating and improving alphazero using population based training"):
  with members:
    - "accelerating self play learning in go"
    - "hyper parameter sweep on alphazero general"
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "population based training of neural networks"
    - "better computer go player with neural network and long term prediction"

Connected through hyperedge HE-3 (induced by source paper "a generalized training approach for multiagent learning"):
  with members:
    - "evolutionary reinforcement learning for sample efficient multiagent coordination"
    - "multiagent evaluation under incomplete information"
    - "emergent coordination through competition"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "open ended learning in symmetric zero sum games"
    - "actor attention critic for multi agent reinforcement learning"
    - "a generalised method for empirical game theoretic analysis"

Connected through hyperedge HE-4 (induced by source paper "alpha alpha rank practically scaling alpha rank through stochastic optimisation"):
  with members:
    - "a generalized training approach for multiagent learning"
    - "multiagent evaluation under incomplete information"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "emergent coordination through competition"
    - "multi agent generalized recursive reasoning"
    - "bi level actor critic for multi agent coordination"
    - "open ended learning in symmetric zero sum games"
    - "maximum a posteriori policy optimisation"

Connected through hyperedge HE-5 (induced by source paper "improving fictitious play reinforcement learning with expanding models"):
  with members:
    - "neural fictitious self play on elf mini rts"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "open ended learning in symmetric zero sum games"
    - "deep counterfactual regret minimization"
    - "balancing two player stochastic games with soft q learning"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"

Connected through hyperedge HE-6 (induced by source paper "rlcard a toolkit for reinforcement learning in card games"):
  with members:
    - "neural mmo a massively multiagent game environment for training and evaluating intelligent agents"
    - "deep counterfactual regret minimization"
    - "unity a general platform for intelligent agents"
    - "virtual taobao virtualizing real world online retail environment for reinforcement learning"
    - "safe and nested subgame solving for imperfect information games"
    - "starcraft ii a new challenge for reinforcement learning"
    - "deep reinforcement learning an overview"
    - "a unified game theoretic approach to multiagent reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "alternative function approximation parameterizations for solving games an analysis of f regression counterfactual regret minimization"):
  with members:
    - "single deep counterfactual regret minimization"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "neural replicator dynamics"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "regret circuits composability of regret minimizers"
    - "deep counterfactual regret minimization"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "deep reinforcement learning from self play in imperfect information games"

Connected through hyperedge HE-8 (induced by source paper "human robot collaboration via deep reinforcement learning of real world interactions"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "probabilistically safe robot planning with confidence based human predictions"
    - "shared autonomy via deep reinforcement learning"
    - "learning to walk via deep reinforcement learning"
    - "a survey on policy search algorithms for learning robot controllers in a handful of trials"
    - "socially aware motion planning with deep reinforcement learning"
    - "sim to real robot learning from pixels with progressive nets"

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)