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 "an atari model zoo for analyzing visualizing and comparing deep reinforcement learning agents"):
  Abstract: Much human and computational effort has aimed to improve how deep reinforcement learning algorithms perform on benchmarks such as the Atari Learning Environment. Comparatively less effort has focused on understanding what has been learned by such methods, and investigating and comparing the representations learned by different families of reinforcement learning (RL) algorithms. Sources of friction include the onerous computational requirements, and general logistical and architectural complications for running Deep RL algorithms at scale. We lessen this friction, by (1) training several algorithms at scale and releasing trained models, (2) integrating with a previous Deep RL model release, and (3) releasing code that makes it easy for anyone to load, visualize, and analyze such models. This paper introduces the Atari Zoo framework, which contains models trained across benchmark Atari games, in an easy-to-use format, as well as code that implements common modes of analysis and connects such models to a popular neural network visualization library. Further, to demonstrate the potential of this dataset and software package, we show initial quantitative and qualitative comparisons between the performance and representations of several deep RL algorithms, highlighting interesting and previously unknown distinctions between them.

Includes members:
  Member "impala scalable distributed deep rl with importance weighted actor learner architectures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "towards better interpretability in deep q networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "distributed prioritized experience replay":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "rainbow combining improvements in deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "a distributional perspective on reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the hanabi challenge a new frontier for ai research")
  Member "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neuroevolution of self interpretable agents")
  Member "visualizing and understanding atari agents":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")

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.NE(Neural and Evolutionary Computing)