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: "visual hindsight experience replay"
  Abstract: Reinforcement Learning algorithms typically require millions of environment interactions to learn successful policies in sparse reward settings. Hindsight Experience Replay (HER) was introduced as a technique to increase sample efficiency through re-imagining unsuccessful trajectories as successful ones by replacing the originally intended goals. However, this method is not applicable to visual domains where the goal configuration is unknown and must be inferred from observation. In this work, we show how unsuccessful visual trajectories can be hallucinated to be successful using a generative model trained on relatively few snapshots of the goal. As far as we are aware, this is the first work that does so with the agent policy conditioned solely on its state. We then apply this model to training reinforcement learning agents in discrete and continuous settings. We show results on a navigation and pick-and-place task in a 3D environment and on a simulated robotics application. Our method shows marked improvement over standard RL algorithms and baselines derived from prior work.

Connected through hyperedge HE-1 (induced by source paper "universal value density estimation for imitation learning and goal conditioned reinforcement learning"):
  with members:
    - "sqil imitation learning via reinforcement learning with sparse rewards"
    - "random expert distillation imitation learning via expert policy support estimation"
    - "generative predecessor models for sample efficient imitation learning"
    - "is the policy gradient a gradient"
    - "sqil imitation learning via regularized behavioral cloning"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-2 (induced by source paper "self educated language agent with hindsight experience replay for instruction following"):
  with members:
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "learning to follow directions in street view"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "actrce augmenting experience via teacher s advice for multi goal reinforcement learning"
    - "competitive experience replay"
    - "a survey of reinforcement learning informed by natural language"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"

Connected through hyperedge HE-3 (induced by source paper "object oriented state editing for hrl"):
  with members:
    - "visual reinforcement learning with imagined goals"
    - "unity a general platform for intelligent agents"
    - "relational inductive biases deep learning and graph networks"
    - "hindsight experience replay"
    - "feudal networks for hierarchical reinforcement learning"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "learning and transfer of modulated locomotor controllers"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

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)