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 "biologically inspired architectures for sample efficient deep reinforcement learning"):
  Abstract: Deep reinforcement learning requires a heavy price in terms of sample efficiency and overparameterization in the neural networks used for function approximation. In this work, we use tensor factorization in order to learn more compact representation for reinforcement learning policies. We show empirically that in the low-data regime, it is possible to learn online policies with 2 to 10 times less total coefficients, with little to no loss of performance. We also leverage progress in second order optimization, and use the theory of wavelet scattering to further reduce the number of learned coefficients, by foregoing learning the topmost convolutional layer filters altogether. We evaluate our results on the Atari suite against recent baseline algorithms that represent the state-of-the-art in data efficiency, and get comparable results with an order of magnitude gain in weight parsimony.

Includes members:
  Member "random expert distillation imitation learning via expert policy support estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "universal value density estimation for imitation learning and goal conditioned reinforcement learning")
  Member "weight agnostic neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning curiosity algorithms")
  Member "scattering networks for hybrid representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "harmonic convolutional networks based on discrete cosine transform")
  Member "when to use parametric models in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an optimistic perspective on offline reinforcement learning")
  Member "model based reinforcement learning for atari":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "kymatio scattering transforms in python":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "demucs deep extractor for music sources with extra unlabeled data remixed")
  Member "generative networks as inverse problems with scattering transforms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scattering networks for hybrid representation learning")
  Member "playing atari with six neurons":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neuroevolution of self interpretable agents")

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)