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: "self imitation learning"
  Abstract: This paper proposes Self-Imitation Learning (SIL), a simple off-policy actor-critic algorithm that learns to reproduce the agent's past good decisions. This algorithm is designed to verify our hypothesis that exploiting past good experiences can indirectly drive deep exploration. Our empirical results show that SIL significantly improves advantage actor-critic (A2C) on several hard exploration Atari games and is competitive to the state-of-the-art count-based exploration methods. We also show that SIL improves proximal policy optimization (PPO) on MuJoCo 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 "population guided parallel policy search for reinforcement learning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "scalable coordinated exploration in concurrent reinforcement learning"
    - "coordinated exploration in concurrent reinforcement learning"
    - "evolution guided policy gradient in reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "rewriting history with inverse rl hindsight inference for policy improvement"):
  with members:
    - "gradient surgery for multi task learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "learning to reach goals without reinforcement learning"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "learning latent plans from play"
    - "advantage weighted regression simple and scalable off policy reinforcement learning"
    - "deepmind control suite"

Connected through hyperedge HE-4 (induced by source paper "sample efficient reinforcement learning through learning from demonstrations in minecraft"):
  with members:
    - "minerl a large scale dataset of minecraft demonstrations"
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "experience replay for continual learning"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "on solving cooperative marl problems with a few good experiences"):
  with members:
    - "learning existing social conventions via observationally augmented self play"
    - "counterfactual multi agent policy gradients"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "deep reinforcement learning from self play in imperfect information games"
    - "improved memory bounded dynamic programming for decentralized pomdps"

Connected through hyperedge HE-6 (induced by source paper "go explore a new approach for hard exploration problems"):
  with members:
    - "fractal ai a fragile theory of intelligence"
    - "exploration by random network distillation"
    - "backplay man muss immer umkehren"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-7 (induced by source paper "self imitation learning via trajectory conditioned policy for hard exploration tasks"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "behaviour suite for reinforcement learning"
    - "skew fit state covering self supervised reinforcement learning"
    - "making efficient use of demonstrations to solve hard exploration problems"
    - "generative adversarial self imitation learning"
    - "exploration by random network distillation"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-8 (induced by source paper "ranking policy gradient"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "if maxent rl is the answer what is the question"
    - "dopamine a research framework for deep reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "implicit quantile networks for distributional reinforcement learning"
    - "variational bayesian reinforcement learning with regret bounds"
    - "are deep policy gradient algorithms truly policy gradient algorithms"
    - "rainbow combining improvements in 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)