human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "sample efficient deep reinforcement learning via episodic backward update"
  Abstract: We propose Episodic Backward Update - a new algorithm to boost the performance of a deep reinforcement learning agent by a fast reward propagation. In contrast to the conventional use of the experience replay with uniform random sampling, our agent samples a whole episode and successively propagates the value of a state to its previous states. Our computationally efficient recursive algorithm allows sparse and delayed rewards to propagate efficiently through all transitions of a sampled episode. We evaluate our algorithm on 2D MNIST Maze environment and 49 games of the Atari 2600 environment and show that our method improves sample efficiency with a competitive amount of computational cost.

Connected through hyperedge HE-1 (induced by source paper "a short survey on memory based reinforcement learning"):
  with members:
    - "episodic memory deep q networks"
    - "episodic curiosity through reachability"
    - "unsupervised predictive memory in a goal directed agent"
    - "attention is all you need"
    - "neural map structured memory for deep reinforcement learning"
    - "memory augmented control networks"
    - "curiosity driven exploration by self supervised prediction"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "prioritized sequence experience replay"):
  with members:
    - "dopamine a research framework for deep reinforcement learning"
    - "experience replay using transition sequences"
    - "deep reinforcement learning"
    - "distributed prioritized experience replay"
    - "a deeper look at experience replay"
    - "a distributional perspective on reinforcement learning"
    - "noisy networks for exploration"
    - "sample efficient actor critic with experience replay"

Question: Please tell me which class the center node 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)