human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "discorl continual reinforcement learning via policy distillation"):
  Abstract: In multi-task reinforcement learning there are two main challenges: at training time, the ability to learn different policies with a single model; at test time, inferring which of those policies applying without an external signal. In the case of continual reinforcement learning a third challenge arises: learning tasks sequentially without forgetting the previous ones. In this paper, we tackle these challenges by proposing DisCoRL, an approach combining state representation learning and policy distillation. We experiment on a sequence of three simulated 2D navigation tasks with a 3 wheel omni-directional robot. Moreover, we tested our approach's robustness by transferring the final policy into a real life setting. The policy can solve all tasks and automatically infer which one to run.

Includes members:
  Member "continual learning for robotics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automatic curriculum learning for deep rl a short survey")
  Member "s trigger continual state representation learning via self triggered generative replay":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "continual reinforcement learning deployed in real life using policy distillation and sim2real transfer")
  Member "continual reinforcement learning in 3d non stationary environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "s rl toolbox environments datasets and evaluation metrics for state representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to navigate using mid level visual priors")
  Member "sim to real reinforcement learning for deformable object manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "continuous learning in single incremental task scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to continually learn")
  Member "state representation learning for control an overview":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "life long disentangled representation learning with cross domain latent homologies":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "are disentangled representations helpful for abstract visual reasoning")

Question: Please tell me which class the source paper 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)