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: "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
  Abstract: The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge of building AI agents with general competency across dozens of Atari 2600 games. It supports a variety of different problem settings and it has been receiving increasing attention from the scientific community, leading to some high-profile success stories such as the much publicized Deep Q-Networks (DQN). In this article we take a big picture look at how the ALE is being used by the research community. We show how diverse the evaluation methodologies in the ALE have become with time, and highlight some key concerns when evaluating agents in the ALE. We use this discussion to present some methodological best practices and provide new benchmark results using these best practices. To further the progress in the field, we introduce a new version of the ALE that supports multiple game modes and provides a form of stochasticity we call sticky actions. We conclude this big picture look by revisiting challenges posed when the ALE was introduced, summarizing the state-of-the-art in various problems and highlighting problems that remain open.

Connected through hyperedge HE-1 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "torchbeast a pytorch platform for distributed rl"
    - "scheduled intrinsic drive a hierarchical take on intrinsically motivated exploration"
    - "craftassist instruction parsing semantic parsing for a minecraft assistant"
    - "unsupervised discovery of decision states for transfer in reinforcement learning"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "count based exploration with the successor representation"
    - "exploration by random network distillation"

Connected through hyperedge HE-2 (induced by source paper "the hanabi challenge a new frontier for ai research"):
  with members:
    - "re determinizing information set monte carlo tree search in hanabi"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "evolving agents for the hanabi 2018 cig competition"

Connected through hyperedge HE-3 (induced by source paper "interference and generalization in temporal difference learning"):
  with members:
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "towards characterizing divergence in deep q learning"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "stiffness a new perspective on generalization in neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "on first order meta learning algorithms"
    - "natural environment benchmarks for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "discor corrective feedback in reinforcement learning via distribution correction"):
  with members:
    - "gradient surgery for multi task learning"
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "towards characterizing divergence in deep q learning"
    - "behavior regularized offline reinforcement learning"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "provably efficient q learning with function approximation via distribution shift error checking oracle"
    - "ray interference a source of plateaus in deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "an optimistic perspective on offline reinforcement learning"):
  with members:
    - "keep doing what worked behavioral modelling priors for offline reinforcement learning"
    - "benchmarking batch deep reinforcement learning algorithms"
    - "off policy policy gradient with state distribution correction"
    - "behavior regularized offline reinforcement learning"
    - "when to use parametric models in reinforcement learning"
    - "challenges of real world reinforcement learning"
    - "model based reinforcement learning for atari"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"

Connected through hyperedge HE-6 (induced by source paper "a visual communication map for multi agent deep reinforcement learning"):
  with members:
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "lenient multi agent deep reinforcement learning"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "adversarial advantage actor critic model for task completion dialogue policy learning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "safe multi agent reinforcement learning for autonomous driving"

Connected through hyperedge HE-7 (induced by source paper "optimistic exploration even with a pessimistic initialisation"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "count based exploration with the successor representation"
    - "exploration by random network distillation"
    - "learning latent dynamics for planning from pixels"
    - "dora the explorer directed outreaching reinforcement action selection"
    - "model based active exploration"
    - "is q learning provably efficient"

Connected through hyperedge HE-8 (induced by source paper "self supervised object level deep reinforcement learning"):
  with members:
    - "space unsupervised object oriented scene representation via spatial attention and decomposition"
    - "entity abstraction in visual model based reinforcement learning"
    - "unsupervised state representation learning in atari"
    - "model based reinforcement learning for atari"
    - "multi object representation learning with iterative variational inference"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "object sensitive deep reinforcement learning"
    - "unsupervised video object segmentation for deep reinforcement learning"

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)