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 "deep neural linear bandits overcoming catastrophic forgetting through likelihood matching"):
  Abstract: We study the neural-linear bandit model for solving sequential decision-making problems with high dimensional side information. Neural-linear bandits leverage the representation power of deep neural networks and combine it with efficient exploration mechanisms, designed for linear contextual bandits, on top of the last hidden layer. Since the representation is being optimized during learning, information regarding exploration with "old" features is lost. Here, we propose the first limited memory neural-linear bandit that is resilient to this phenomenon, which we term catastrophic forgetting. We evaluate our method on a variety of real-world data sets, including regression, classification, and sentiment analysis, and observe that our algorithm is resilient to catastrophic forgetting and achieves superior performance.

Includes members:
  Member "learn what not to learn action elimination with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "efficient exploration through bayesian deep q networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "shallow updates for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "kalman meets bellman improving policy evaluation through value tracking")
  Member "the uncertainty bellman equation and exploration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "a tutorial on thompson sampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "curiosity driven exploration by self supervised prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "graying the black box understanding dqns":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "overcoming catastrophic forgetting in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on contextual embeddings")

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)