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 "sample based distributional policy gradient"):
  Abstract: Distributional reinforcement learning (DRL) is a recent reinforcement learning framework whose success has been supported by various empirical studies. It relies on the key idea of replacing the expected return with the return distribution, which captures the intrinsic randomness of the long term rewards. Most of the existing literature on DRL focuses on problems with discrete action space and value based methods. In this work, motivated by applications in robotics with continuous action space control settings, we propose sample-based distributional policy gradient (SDPG) algorithm. It models the return distribution using samples via a reparameterization technique widely used in generative modeling and inference. We compare SDPG with the state-of-art policy gradient method in DRL, distributed distributional deterministic policy gradients (D4PG), which has demonstrated state-of-art performance. We apply SDPG and D4PG to multiple OpenAI Gym environments and observe that our algorithm shows better sample efficiency as well as higher reward for most tasks.

Includes members:
  Member "gan based deep distributional reinforcement learning for resource management in network slicing":
    also connected through other hyperedges:
      (none)
  Member "deepmind control suite":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "quota the quantile option architecture for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributional reinforcement learning for efficient exploration")
  Member "nonlinear distributional gradient temporal difference learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributional policy optimization an alternative approach for continuous control")
  Member "implicit quantile networks for distributional reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "challenges of real world reinforcement learning")
  Member "distributed distributional deterministic policy gradients":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")
  Member "generative modeling using the sliced wasserstein distance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning implicit generative models with theoretical guarantees")
  Member "distributional multivariate policy evaluation and exploration with the bellman gan":
    also connected through other hyperedges:
      (none)

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)