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: "third person visual imitation learning via decoupled hierarchical controller"
  Abstract: We study a generalized setup for learning from demonstration to build an agent that can manipulate novel objects in unseen scenarios by looking at only a single video of human demonstration from a third-person perspective. To accomplish this goal, our agent should not only learn to understand the intent of the demonstrated third-person video in its context but also perform the intended task in its environment configuration. Our central insight is to enforce this structure explicitly during learning by decoupling what to achieve (intended task) from how to perform it (controller). We propose a hierarchical setup where a high-level module learns to generate a series of first-person sub-goals conditioned on the third-person video demonstration, and a low-level controller predicts the actions to achieve those sub-goals. Our agent acts from raw image observations without any access to the full state information. We show results on a real robotic platform using Baxter for the manipulation tasks of pouring and placing objects in a box. Project video and code are at this https URL

Connected through hyperedge HE-1 (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"
    - "zero shot visual imitation"
    - "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-2 (induced by source paper "a mobile robot hand arm teleoperation system by vision and imu"):
  with members:
    - "avid learning multi stage tasks via pixel level translation of human videos"
    - "dexpilot vision based teleoperation of dexterous robotic hand arm system"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "vision based teleoperation of shadow dexterous hand using end to end deep neural network"
    - "toward multimodal image to image translation"
    - "image to image translation with conditional adversarial networks"
    - "context encoders feature learning by inpainting"
    - "spatial transformer networks"

Connected through hyperedge HE-3 (induced by source paper "avid learning multi stage tasks via pixel level translation of human videos"):
  with members:
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "imitating latent policies from observation"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "learning latent dynamics for planning from pixels"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "state representation learning for control an overview"
    - "solar deep structured latent representations for model based reinforcement learning"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

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)