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: "adaptive information gathering via imitation learning"
  Abstract: In the adaptive information gathering problem, a policy is required to select an informative sensing location using the history of measurements acquired thus far. While there is an extensive amount of prior work investigating effective practical approximations using variants of Shannon's entropy, the efficacy of such policies heavily depends on the geometric distribution of objects in the world. On the other hand, the principled approach of employing online POMDP solvers is rendered impractical by the need to explicitly sample online from a posterior distribution of world maps. #R##N#We present a novel data-driven imitation learning framework to efficiently train information gathering policies. The policy imitates a clairvoyant oracle - an oracle that at train time has full knowledge about the world map and can compute maximally informative sensing locations. We analyze the learnt policy by showing that offline imitation of a clairvoyant oracle is implicitly equivalent to online oracle execution in conjunction with posterior sampling. This observation allows us to obtain powerful near-optimality guarantees for information gathering problems possessing an adaptive sub-modularity property. As demonstrated on a spectrum of 2D and 3D exploration problems, the trained policies enjoy the best of both worlds - they adapt to different world map distributions while being computationally inexpensive to evaluate.

Connected through hyperedge HE-1 (induced by source paper "mpc net a first principles guided policy search"):
  with members:
    - "feedback mpc for torque controlled legged robots"
    - "data efficient reinforcement learning for legged robots"
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "on the convergence of adam and beyond"
    - "learning agile and dynamic motor skills for legged robots"
    - "policies modulating trajectory generators"
    - "an algorithmic perspective on imitation learning"
    - "sim to real learning agile locomotion for quadruped robots"

Connected through hyperedge HE-2 (induced by source paper "learn to score efficient 3d scene exploration by predicting view utility"):
  with members:
    - "plan3d viewpoint and trajectory optimization for aerial multi view stereo reconstruction"
    - "submodular trajectory optimization for aerial 3d scanning"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "generic 3d representation via pose estimation and matching"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "a reinforcement learning approach to the view planning problem"
    - "identity mappings in deep residual networks"
    - "octnet learning deep 3d representations at high resolutions"

Connected through hyperedge HE-3 (induced by source paper "learning neural parsers with deterministic differentiable imitation learning"):
  with members:
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "learning heuristic search via imitation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "continuous control with deep reinforcement learning"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "off policy actor critic"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-4 (induced by source paper "deeply aggrevated differentiable imitation learning for sequential prediction"):
  with members:
    - "deep reinforcement learning for dialogue generation"
    - "generative adversarial imitation learning"
    - "an actor critic algorithm for sequence prediction"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "plato policy learning using adaptive trajectory optimization"
    - "benchmarking deep reinforcement learning for continuous control"
    - "sequence level training with recurrent neural networks"
    - "learning to search for dependencies"

Connected through hyperedge HE-5 (induced by source paper "learning heuristic search via imitation"):
  with members:
    - "learning from the hindsight plan episodic mpc improvement"
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"
    - "value iteration networks"
    - "learning to gather information via imitation"
    - "prioritized experience replay"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"

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.RO(Robotics)