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: "provably efficient maximum entropy exploration"
  Abstract: Suppose an agent is in a (possibly unknown) Markov Decision Process in the absence of a reward signal, what might we hope that an agent can efficiently learn to do? This work studies a broad class of objectives that are defined solely as functions of the state-visitation frequencies that are induced by how the agent behaves. For example, one natural, intrinsically defined, objective problem is for the agent to learn a policy which induces a distribution over state space that is as uniform as possible, which can be measured in an entropic sense. We provide an efficient algorithm to optimize such such intrinsically defined objectives, when given access to a black box planning oracle (which is robust to function approximation). Furthermore, when restricted to the tabular setting where we have sample based access to the MDP, our proposed algorithm is provably efficient, both in terms of its sample and computational complexities. Key to our algorithmic methodology is utilizing the conditional gradient method (a.k.a. the Frank-Wolfe algorithm) which utilizes an approximate MDP solver.

Connected through hyperedge HE-1 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "go explore a new approach for hard exploration problems"
    - "skew fit state covering self supervised reinforcement learning"
    - "dynamics aware unsupervised discovery of skills"
    - "self supervised learning of image embedding for continuous control"
    - "momentum contrast for unsupervised visual representation learning"
    - "learning latent plans from play"

Connected through hyperedge HE-2 (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-3 (induced by source paper "exploring unknown states with action balance"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "a survey on intrinsic motivation in reinforcement learning"
    - "benchmarking bonus based exploration methods on the arcade learning environment"
    - "exploration by random network distillation"
    - "is q learning provably efficient"
    - "count based exploration with neural density models"
    - "count based exploration in feature space for reinforcement learning"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-4 (induced by source paper "reward free exploration for reinforcement learning"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "reinforcement learning with convex constraints"
    - "information theoretic considerations in batch reinforcement learning"
    - "provably efficient exploration in policy optimization"
    - "kinematic state abstraction and provably efficient rich observation reinforcement learning"
    - "reward constrained policy optimization"

Connected through hyperedge HE-5 (induced by source paper "efficient exploration via state marginal matching"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "robel robotics benchmarks for learning with low cost robots"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "concurrent meta reinforcement learning"
    - "benchmarking bonus based exploration methods on the arcade learning environment"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "exploration by random network distillation"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-6 (induced by source paper "skew fit state covering self supervised reinforcement learning"):
  with members:
    - "self supervised learning of image embedding for continuous control"
    - "exploration by random network distillation"
    - "temporal difference models model free deep rl for model based control"
    - "unsupervised meta learning for reinforcement learning"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "large scale study of curiosity driven learning"
    - "curiosity driven experience prioritization via density estimation"

Connected through hyperedge HE-7 (induced by source paper "smirl surprise minimizing rl in dynamic environments"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "self supervised exploration via disagreement"
    - "imitating latent policies from observation"
    - "exploration by random network distillation"
    - "playing hard exploration games by watching youtube"
    - "large scale study of curiosity driven learning"
    - "model based active exploration"
    - "generative adversarial imitation from observation"

Connected through hyperedge HE-8 (induced by source paper "kinematic state abstraction and provably efficient rich observation reinforcement learning"):
  with members:
    - "sample complexity of reinforcement learning using linearly combined model ensembles"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "information theoretic considerations in batch reinforcement learning"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "ucb exploration via q ensembles"
    - "learning to reason end to end module networks for visual question answering"
    - "ucb and infogain exploration via boldsymbol q ensembles"
    - "curiosity driven exploration by self supervised prediction"

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)