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: "efficient exploration via state marginal matching"
  Abstract: To solve tasks with sparse rewards, reinforcement learning algorithms must be equipped with suitable exploration techniques. However, it is unclear what underlying objective is being optimized by existing exploration algorithms, or how they can be altered to incorporate prior knowledge about the task. Most importantly, it is difficult to use exploration experience from one task to acquire exploration strategies for another task. We address these shortcomings by learning a single exploration policy that can quickly solve a suite of downstream tasks in a multi-task setting, amortizing the cost of learning to explore. We recast exploration as a problem of State Marginal Matching (SMM): we learn a mixture of policies for which the state marginal distribution matches a given target state distribution, which can incorporate prior knowledge about the task. Without any prior knowledge, the SMM objective reduces to maximizing the marginal state entropy. We optimize the objective by reducing it to a two-player, zero-sum game, where we iteratively fit a state density model and then update the policy to visit states with low density under this model. While many previous algorithms for exploration employ a similar procedure, they omit a crucial historical averaging step, without which the iterative procedure does not converge to a Nash equilibria. To parallelize exploration, we extend our algorithm to use mixtures of policies, wherein we discover connections between SMM and previously-proposed skill learning methods based on mutual information. On complex navigation and manipulation tasks, we demonstrate that our algorithm explores faster and adapts more quickly to new tasks.

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 "reinforcement learning through active inference"):
  with members:
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "deep active inference as variational policy gradients"
    - "a unified bellman optimality principle combining reward maximization and empowerment"
    - "deep dynamics models for learning dexterous manipulation"
    - "demystifying active inference"
    - "model based reinforcement learning for atari"
    - "dream to control learning behaviors by latent imagination"
    - "bayesian policy selection using active inference"

Connected through hyperedge HE-3 (induced by source paper "state alignment based imitation learning"):
  with members:
    - "wasserstein adversarial imitation learning"
    - "provably efficient imitation learning from observation alone"
    - "imitation learning as f divergence minimization"
    - "imitating latent policies from observation"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "observe and look further achieving consistent performance on atari"

Connected through hyperedge HE-4 (induced by source paper "if maxent rl is the answer what is the question"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "skew fit state covering self supervised reinforcement learning"
    - "tsallis reinforcement learning a unified framework for maximum entropy reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "unsupervised control through non parametric discriminative rewards"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "variational bayesian reinforcement learning with regret bounds"

Connected through hyperedge HE-5 (induced by source paper "a divergence minimization perspective on imitation learning methods"):
  with members:
    - "imitation learning as f divergence minimization"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "formal limitations on the measurement of mutual information"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-6 (induced by source paper "unsupervised meta learning for reinforcement learning"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "universal planning networks"
    - "temporal difference models model free deep rl for model based control"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "unsupervised learning via meta learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "diversity is all you need learning skills without a reward function"

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)