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: "re evaluating evaluation"
  Abstract: Progress in machine learning is measured by careful evaluation on problems of outstanding common interest. However, the proliferation of benchmark suites and environments, adversarial attacks, and other complications has diluted the basic evaluation model by overwhelming researchers with choices. Deliberate or accidental cherry picking is increasingly likely, and designing well-balanced evaluation suites requires increasing effort. In this paper we take a step back and propose Nash averaging. The approach builds on a detailed analysis of the algebraic structure of evaluation in two basic scenarios: agent-vs-agent and agent-vs-task. The key strength of Nash averaging is that it automatically adapts to redundancies in evaluation data, so that results are not biased by the incorporation of easy tasks or weak agents. Nash averaging thus encourages maximally inclusive evaluation -- since there is no harm (computational cost aside) from including all available tasks and agents.

Connected through hyperedge HE-1 (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"
    - "open ended learning in symmetric zero sum games"

Connected through hyperedge HE-2 (induced by source paper "differentiable game mechanics"):
  with members:
    - "a variational inequality perspective on generative adversarial nets"
    - "global convergence to the equilibrium of gans using variational inequalities"
    - "negative momentum for improved game dynamics"
    - "which training methods for gans do actually converge"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "strongly typed agents are guaranteed to interact safely"
    - "learning with opponent learning awareness"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "emergent coordination through competition"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "deepmind control suite"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "a generalised method for empirical game theoretic analysis"
    - "counterfactual multi agent policy gradients"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "emergent complexity via multi agent competition"

Connected through hyperedge HE-4 (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"
    - "open ended learning in symmetric zero sum games"
    - "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"

Connected through hyperedge HE-5 (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"
    - "open ended learning in symmetric zero sum games"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "dual indicators to analyse ai benchmarks difficulty discrimination ability and generality"):
  with members:
    - "accounting for the neglected dimensions of ai progress"
    - "a multidisciplinary task based perspective for evaluating the impact of ai autonomy and generality on the future of work"
    - "neural episodic control"
    - "the reactor a fast and sample efficient actor critic agent for reinforcement learning"
    - "learning to play in a day faster deep reinforcement learning by optimality tightening"
    - "pgq combining policy gradient and q learning"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "alpha rank multi agent evaluation by evolution"):
  with members:
    - "emergent coordination through competition"
    - "a generalised method for empirical game theoretic analysis"
    - "the exact computational complexity of evolutionarily stable strategies"
    - "learning dynamics and the co evolution of competing sexual species"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "average case performance of replicator dynamics in potential games via computing regions of attraction"

Connected through hyperedge HE-8 (induced by source paper "fast and furious learning in zero sum games vanishing regret with non vanishing step sizes"):
  with members:
    - "alpha rank multi agent evaluation by evolution"
    - "multi agent learning in network zero sum games is a hamiltonian system"
    - "open ended learning in symmetric zero sum games"
    - "the mechanics of n player differentiable games"
    - "learning dynamics and the co evolution of competing sexual species"
    - "cycles in adversarial regularized learning"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"
    - "training gans with optimism"

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)