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: "policy consolidation for continual reinforcement learning"
  Abstract: We propose a method for tackling catastrophic forgetting in deep reinforcement learning that is \textit{agnostic} to the timescale of changes in the distribution of experiences, does not require knowledge of task boundaries, and can adapt in \textit{continuously} changing environments. In our \textit{policy consolidation} model, the policy network interacts with a cascade of hidden networks that simultaneously remember the agent's policy at a range of timescales and regularise the current policy by its own history, thereby improving its ability to learn without forgetting. We find that the model improves continual learning relative to baselines on a number of continuous control tasks in single-task, alternating two-task, and multi-agent competitive self-play settings.

Connected through hyperedge HE-1 (induced by source paper "merging deterministic policy gradient estimations with varied bias variance tradeoff for effective deep reinforcement learning"):
  with members:
    - "regularized anderson acceleration for off policy deep reinforcement learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "a theory of regularized markov decision processes"
    - "importance resampling for off policy prediction"
    - "lookahead optimizer k steps forward 1 step back"
    - "understanding short horizon bias in stochastic meta optimization"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "smoothed action value functions for learning gaussian policies"

Connected through hyperedge HE-2 (induced by source paper "experience replay for continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "closed loop gan for continual learning"
    - "memory efficient experience replay for streaming learning"
    - "selective experience replay for lifelong learning"
    - "continual reinforcement learning with complex synapses"
    - "progressive neural networks"
    - "continual learning with deep generative 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)