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: "loss is its own reward self supervision for reinforcement learning"
  Abstract: Reinforcement learning optimizes policies for expected cumulative reward. Need the supervision be so narrow? Reward is delayed and sparse for many tasks, making it a difficult and impoverished signal for end-to-end optimization. To augment reward, we consider a range of self-supervised tasks that incorporate states, actions, and successors to provide auxiliary losses. These losses offer ubiquitous and instantaneous supervision for representation learning even in the absence of reward. While current results show that learning from reward alone is feasible, pure reinforcement learning methods are constrained by computational and data efficiency issues that can be remedied by auxiliary losses. Self-supervised pre-training and joint optimization improve the data efficiency and policy returns of end-to-end reinforcement learning.

Connected through hyperedge HE-1 (induced by source paper "unsupervised visuomotor control through distributional planning networks"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "temporal difference models model free deep rl for model based control"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"
    - "zero shot visual imitation"
    - "soft actor critic algorithms and applications"
    - "few shot goal inference for visuomotor learning and planning"
    - "unsupervised control through non parametric discriminative rewards"

Connected through hyperedge HE-2 (induced by source paper "discovery of useful questions as auxiliary tasks"):
  with members:
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "general non linear bellman equations"
    - "on inductive biases in deep reinforcement learning"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "unsupervised meta learning for reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "meta gradient reinforcement learning"
    - "unicorn continual learning with a universal off policy agent"

Connected through hyperedge HE-3 (induced by source paper "stochastic latent actor critic deep reinforcement learning with a latent variable model"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "deepmdp learning continuous latent space models for representation learning"
    - "shaping belief states with generative environment models for rl"
    - "deepmind control suite"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning latent dynamics for planning from pixels"
    - "visual reinforcement learning with imagined goals"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"

Connected through hyperedge HE-4 (induced by source paper "learning to navigate using mid level visual priors"):
  with members:
    - "does computer vision matter for action"
    - "habitat a platform for embodied ai research"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "visual memory for robust path following"
    - "taskonomy disentangling task transfer learning"
    - "gibson env real world perception for embodied agents"

Connected through hyperedge HE-5 (induced by source paper "supervise thyself examining self supervised representations in interactive environments"):
  with members:
    - "a theoretical analysis of contrastive unsupervised representation learning"
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "playing hard exploration games by watching youtube"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "state representation learning for control an overview"
    - "deep contextualized word representations"
    - "gotta learn fast a new benchmark for generalization in rl"

Connected through hyperedge HE-6 (induced by source paper "state representation learning from demonstration"):
  with members:
    - "weight agnostic neural networks"
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "deepmind control suite"
    - "world models"
    - "state representation learning for control an overview"
    - "hindsight experience replay"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-7 (induced by source paper "high dimensional control using generalized auxiliary tasks"):
  with members:
    - "samples are not all useful denoising policy gradient updates using variance"
    - "terminal prediction as an auxiliary task for deep reinforcement learning"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "large scale study of curiosity driven learning"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "recurrent world models facilitate policy evolution"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"

Connected through hyperedge HE-8 (induced by source paper "agent modeling as auxiliary task for deep reinforcement learning"):
  with members:
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "backplay man muss immer umkehren"
    - "deep reinforcement learning for general video game ai"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "machine theory of mind"
    - "a deep policy inference q network for multi agent systems"

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)