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: "identification and off policy learning of multiple objectives using adaptive clustering"
  Abstract: Abstract   In this work, we present a methodology that enables an agent to make efficient use of its exploratory actions by autonomously identifying possible objectives in its environment and learning them in parallel. The identification of objectives is achieved using an online and unsupervised adaptive clustering algorithm. The identified objectives are learned (at least partially) in parallel using     Q  −    learning. Using a simulated agent and environment, it is shown that the converged or partially converged value function weights resulting from off-policy learning can be used to accumulate knowledge about multiple objectives without any additional exploration. We claim that the proposed approach could be useful in scenarios where the objectives are initially unknown or in real world scenarios where exploration is typically a time and energy intensive process. The implications and possible extensions of this work are also briefly discussed.

Connected through hyperedge HE-1 (induced by source paper "experience replay using transition sequences"):
  with members:
    - "selective experience replay for lifelong learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "hindsight experience replay"
    - "swarm enabling technology for multi robot systems"
    - "sample efficient actor critic with experience replay"
    - "asynchronous methods for deep reinforcement learning"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "prioritized experience replay"

Connected through hyperedge HE-2 (induced by source paper "self organizing maps for storage and transfer of knowledge in reinforcement learning"):
  with members:
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "adapting low cost platforms for robotics research"
    - "cosine normalization using cosine similarity instead of dot product in neural networks"
    - "online transfer learning in reinforcement learning domains"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "off policy learning with eligibility traces a survey"

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.AI(Artificial Intelligence)