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: "one shot high fidelity imitation training large scale deep nets with rl"
  Abstract: Humans are experts at high-fidelity imitation -- closely mimicking a demonstration, often in one attempt. Humans use this ability to quickly solve a task instance, and to bootstrap learning of new tasks. Achieving these abilities in autonomous agents is an open problem. In this paper, we introduce an off-policy RL algorithm (MetaMimic) to narrow this gap. MetaMimic can learn both (i) policies for high-fidelity one-shot imitation of diverse novel skills, and (ii) policies that enable the agent to solve tasks more efficiently than the demonstrators. MetaMimic relies on the principle of storing all experiences in a memory and replaying these to learn massive deep neural network policies by off-policy RL. This paper introduces, to the best of our knowledge, the largest existing neural networks for deep RL and shows that larger networks with normalization are needed to achieve one-shot high-fidelity imitation on a challenging manipulation task. The results also show that both types of policy can be learned from vision, in spite of the task rewards being sparse, and without access to demonstrator actions.

Connected through hyperedge HE-1 (induced by source paper "scalable multi task imitation learning with autonomous improvement"):
  with members:
    - "unsupervised visuomotor control through distributional planning networks"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "soft actor critic algorithms and applications"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "task embedded control networks for few shot imitation learning"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "hindsight experience replay"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-2 (induced by source paper "making efficient use of demonstrations to solve hard exploration problems"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "reinforced imitation in heterogeneous action space"
    - "benchmarking model based reinforcement learning"
    - "benchmarking bonus based exploration methods on the arcade learning environment"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "off policy deep reinforcement learning without exploration"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-3 (induced by source paper "simitate a hybrid imitation learning benchmark"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "dart noise injection for robust imitation learning"
    - "human action localization with sparse spatial supervision"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "end to end driving via conditional imitation learning"
    - "one shot imitation learning"

Connected through hyperedge HE-4 (induced by source paper "watch try learn meta learning from demonstrations and reward"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "hierarchical imitation and reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "task embedded control networks for few shot imitation learning"

Connected through hyperedge HE-5 (induced by source paper "playing hard exploration games by watching youtube"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "zero shot visual imitation"
    - "semi parametric topological memory for navigation"
    - "observe and look further achieving consistent performance on atari"
    - "distributed prioritized experience replay"
    - "distributed distributional deterministic policy gradients"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "representation learning with contrastive predictive coding"

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)