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: "the hanabi challenge a new frontier for ai research"
  Abstract: Abstract   From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agents reaching superhuman performance in challenge domains like Go, Atari, and some variants of poker. As with their predecessors of chess, checkers, and backgammon, these game domains have driven research by providing sophisticated yet well-defined challenges for artificial intelligence practitioners. We continue this tradition by proposing the game of Hanabi as a new challenge domain with novel problems that arise from its combination of purely cooperative gameplay with two to five players and imperfect information. In particular, we argue that Hanabi elevates reasoning about the beliefs and intentions of other agents to the foreground. We believe developing novel techniques for such theory of mind reasoning will not only be crucial for success in Hanabi, but also in broader collaborative efforts, especially those with human partners. To facilitate future research, we introduce the open-source Hanabi Learning Environment, propose an experimental framework for the research community to evaluate algorithmic advances, and assess the performance of current state-of-the-art techniques.

Connected through hyperedge HE-1 (induced by source paper "other play for zero shot coordination"):
  with members:
    - "adversarially guided self play for adopting social conventions"
    - "plannable approximations to mdp homomorphisms equivariance under actions"
    - "improving policies via search in cooperative partially observable games"
    - "on the utility of learning about humans for human ai coordination"
    - "to populate is to regulate"
    - "simplified action decoder for deep multi agent reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "theory of minds understanding behavior in groups through inverse planning"

Connected through hyperedge HE-2 (induced by source paper "learning to resolve alliance dilemmas in many player zero sum games"):
  with members:
    - "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"
    - "open ended learning in symmetric zero sum games"
    - "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 "regret bounds for decentralized learning in cooperative multi agent dynamical systems"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "on applications of bootstrap in continuous space reinforcement learning"
    - "on optimality of adaptive linear quadratic regulators"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "finite time analysis of optimal adaptive policies for linear quadratic systems"
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "learning unknown markov decision processes a thompson sampling approach"

Connected through hyperedge HE-4 (induced by source paper "social diversity and social preferences in mixed motive reinforcement learning"):
  with members:
    - "on the utility of learning about humans for human ai coordination"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "evolving intrinsic motivations for altruistic behavior"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-5 (induced by source paper "adversarially guided self play for adopting social conventions"):
  with members:
    - "learning existing social conventions via observationally augmented self play"
    - "a style based generator architecture for generative adversarial networks"
    - "context aware captions from context agnostic supervision"
    - "emergence of grounded compositional language in multi agent populations"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "image to image translation with conditional adversarial networks"
    - "learning to communicate with deep multi agent reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "openspiel a framework for reinforcement learning in games"):
  with members:
    - "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"
    - "re evaluating evaluation"

Connected through hyperedge HE-7 (induced by source paper "neural replicator dynamics"):
  with members:
    - "alpha rank multi agent evaluation by evolution"
    - "alternative function approximation parameterizations for solving games an analysis of f regression counterfactual regret minimization"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "revisiting cfr and alternating updates"
    - "deep counterfactual regret minimization"
    - "automated curriculum learning for neural networks"

Connected through hyperedge HE-8 (induced by source paper "simplified action decoder for deep multi agent reinforcement learning"):
  with members:
    - "re determinizing information set monte carlo tree search in hanabi"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "distributed prioritized experience replay"
    - "counterfactual multi agent policy gradients"

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)