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 "attraction repulsion actor critic for continuous control reinforcement learning"):
  Abstract: Continuous control tasks in reinforcement learning are important because they provide an important framework for learning in high-dimensional state spaces with deceptive rewards, where the agent can easily become trapped into suboptimal solutions. One way to avoid local optima is to use a population of agents to ensure coverage of the policy space, yet learning a population with the "best" coverage is still an open problem. In this work, we present a novel approach to population-based RL in continuous control that leverages properties of normalizing flows to perform attractive and repulsive operations between current members of the population and previously observed policies. Empirical results on the MuJoCo suite demonstrate a high performance gain for our algorithm compared to prior work, including Soft-Actor Critic (SAC).

Includes members:
  Member "collaborative evolutionary reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "leveraging exploration in off policy algorithms via normalizing flows":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models")
  Member "cem rl combining evolutionary and gradient based methods for policy search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "randomized value functions via multiplicative normalizing flows":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "benchmarking bonus based exploration methods on the arcade learning environment")
  Member "evolution guided policy gradient in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "meta reinforcement learning of structured exploration strategies":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "meta learning curiosity algorithms")
  Member "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "boosting trust region policy optimization by normalizing flows policy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "recvae a new variational autoencoder for top n recommendations with implicit feedback")

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)