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 "ex2 exploration with exemplar models for deep reinforcement learning"):
  Abstract: Deep reinforcement learning algorithms have been shown to learn complex tasks using highly general policy classes. However, sparse reward problems remain a significant challenge. Exploration methods based on novelty detection have been particularly successful in such settings but typically require generative or predictive models of the observations, which can be difficult to train when the observations are very high-dimensional and complex, as in the case of raw images. We propose a novelty detection algorithm for exploration that is based entirely on discriminatively trained exemplar models, where classifiers are trained to discriminate each visited state against all others. Intuitively, novel states are easier to distinguish against other states seen during training. We show that this kind of discriminative modeling corresponds to implicit density estimation, and that it can be combined with count-based exploration to produce competitive results on a range of popular benchmark tasks, including state-of-the-art results on challenging egocentric observations in the vizDoom benchmark.

Includes members:
  Member "surprise based intrinsic motivation for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "curiosity driven exploration by self supervised prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "stochastic neural networks for hierarchical reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meta learning curiosity algorithms")
  Member "exploratory gradient boosting for reinforcement learning in complex domains":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the minerl competition on sample efficient reinforcement learning using human priors a retrospective")
  Member "deep exploration via bootstrapped dqn":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "learning and transfer of modulated locomotor controllers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "following instructions by imagining and reaching visual goals")
  Member "improved techniques for training gans":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")

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)