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: "zero shot visual imitation"
  Abstract: The current dominant paradigm for imitation learning relies on strong supervision of expert actions to learn both 'what' and 'how' to imitate. We pursue an alternative paradigm wherein an agent first explores the world without any expert supervision and then distills its experience into a goal-conditioned skill policy with a novel forward consistency loss. In our framework, the role of the expert is only to communicate the goals (i.e., what to imitate) during inference. The learned policy is then employed to mimic the expert (i.e., how to imitate) after seeing just a sequence of images demonstrating the desired task. Our method is 'zero-shot' in the sense that the agent never has access to expert actions during training or for the task demonstration at inference. We evaluate our zero-shot imitator in two real-world settings: complex rope manipulation with a Baxter robot and navigation in previously unseen office environments with a TurtleBot. Through further experiments in VizDoom simulation, we provide evidence that better mechanisms for exploration lead to learning a more capable policy which in turn improves end task performance. Videos, models, and more details are available at this https URL

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"
    - "visual hindsight experience replay"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-2 (induced by source paper "learning view and target invariant visual servoing for navigation"):
  with members:
    - "neural autonomous navigation with riemannian motion policy"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "visual memory for robust path following"
    - "gaple generalizable approaching policy learning for robotic object searching in indoor environment"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "superpoint self supervised interest point detection and description"

Connected through hyperedge HE-3 (induced by source paper "a geometric perspective on visual imitation learning"):
  with members:
    - "keto learning keypoint representations for tool manipulation"
    - "visual geometric skill inference by watching human demonstration"
    - "self supervised correspondence in visuomotor policy learning"
    - "graph structured visual imitation"
    - "third person visual imitation learning via decoupled hierarchical controller"
    - "robot eye hand coordination learning by watching human demonstrations a task function approximation approach"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-4 (induced by source paper "robonet large scale multi robot learning"):
  with members:
    - "self supervised exploration via disagreement"
    - "learning latent plans from play"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "manipulation by feel touch based control with deep predictive models"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "unsupervised visuomotor control through distributional planning networks"
    - "scaling autoregressive video models"
    - "exploration by random network distillation"

Connected through hyperedge HE-5 (induced by source paper "bayesian relational memory for semantic visual navigation"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "composable planning with attributes"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "iterative visual reasoning beyond convolutions"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-6 (induced by source paper "unsupervised curricula for visual meta reinforcement learning"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "exploration by random network distillation"
    - "unsupervised meta learning for reinforcement learning"
    - "visual reinforcement learning with imagined goals"
    - "deep clustering for unsupervised learning of visual features"
    - "model based active exploration"
    - "unsupervised learning via meta learning"

Connected through hyperedge HE-7 (induced by source paper "divis domain invariant visual servoing for collision free goal reaching"):
  with members:
    - "vr goggles for robots real to sim domain adaptation for visual control"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "speaker follower models for vision and language navigation"
    - "viser visual self regularization"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"

Connected through hyperedge HE-8 (induced by source paper "self imitation learning via trajectory conditioned policy for hard exploration tasks"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "behaviour suite for reinforcement learning"
    - "skew fit state covering self supervised reinforcement learning"
    - "making efficient use of demonstrations to solve hard exploration problems"
    - "generative adversarial self imitation learning"
    - "exploration by random network distillation"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
    - "playing hard exploration games by watching youtube"

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)