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: "open ended learning in symmetric zero sum games"
  Abstract: Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them `winner' and `loser'. If the game is approximately transitive, then self-play generates sequences of agents of increasing strength. However, nontransitive games, such as rock-paper-scissors, can exhibit strategic cycles, and there is no longer a clear objective -- we want agents to increase in strength, but against whom is unclear. In this paper, we introduce a geometric framework for formulating agent objectives in zero-sum games, in order to construct adaptive sequences of objectives that yield open-ended learning. The framework allows us to reason about population performance in nontransitive games, and enables the development of a new algorithm (rectified Nash response, PSRO_rN) that uses game-theoretic niching to construct diverse populations of effective agents, producing a stronger set of agents than existing algorithms. We apply PSRO_rN to two highly nontransitive resource allocation games and find that PSRO_rN consistently outperforms the existing alternatives.

Connected through hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies"):
  with members:
    - "on empirical comparisons of optimizers for deep learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "on the variance of the adaptive learning rate and beyond"
    - "the visual task adaptation benchmark"
    - "using learned optimizers to make models robust to input noise"
    - "deepobs a deep learning optimizer benchmark suite"
    - "nas bench 101 towards reproducible neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "learning to resolve alliance dilemmas in many player zero sum games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "options as responses grounding behavioural hierarchies in multi agent rl"
    - "learning reciprocity in complex sequential social dilemmas"
    - "emergent coordination through competition"
    - "no press diplomacy modeling multi agent gameplay"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "evolving intrinsic motivations for altruistic behavior"

Connected through hyperedge HE-3 (induced by source paper "arena a general evaluation platform and building toolkit for multi agent intelligence"):
  with members:
    - "neural mmo a massively multiagent game environment for training and evaluating intelligent agents"
    - "marathon environments multi agent continuous control benchmarks in a modern video game engine"
    - "emergent coordination through competition"
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "habitat a platform for embodied ai research"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "dota 2 with large scale deep reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "growing a brain fine tuning by increasing model capacity"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "an empirical model of large batch training"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "openspiel a framework for reinforcement learning in games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "rethinking formal models of partially observable multiagent decision making"
    - "behaviour suite for reinforcement learning"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "a generalised method for empirical game theoretic analysis"
    - "re evaluating evaluation"

Connected through hyperedge HE-6 (induced by source paper "options as responses grounding behavioural hierarchies in multi agent rl"):
  with members:
    - "alpha rank multi agent evaluation by evolution"
    - "feudal multi agent hierarchies for cooperative reinforcement learning"
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "hierarchical visuomotor control of humanoids"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "actor attention critic for multi agent reinforcement learning"
    - "re evaluating evaluation"

Connected through hyperedge HE-7 (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"
    - "openspiel a framework for reinforcement learning in games"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "actor attention critic for multi agent reinforcement learning"
    - "a generalised method for empirical game theoretic analysis"

Connected through hyperedge HE-8 (induced by source paper "adversarial policies attacking deep reinforcement learning"):
  with members:
    - "on the geometry of adversarial examples"
    - "feature denoising for improving adversarial robustness"
    - "deep counterfactual regret minimization"
    - "verifying controllers against adversarial examples with bayesian optimization"
    - "are adversarial examples inevitable"
    - "verifiable reinforcement learning via policy extraction"
    - "motivating the rules of the game for adversarial example research"
    - "adversarial attacks on neural network policies"

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)