human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "data driven planning via imitation learning"):
  Abstract: Robot planning is the process of selecting a sequence of actions that optimize for a task specific objective. The optimal solutions to such tasks are heavily influenced by the implicit structure in the environment, i.e. the configuration of objects in the world. State-of-the-art planning approaches, however, do not exploit this structure, thereby expending valuable effort searching the action space instead of focusing on potentially good actions. In this paper, we address the problem of enabling planners to adapt their search strategies by inferring such good actions in an efficient manner using only the information uncovered by the search up until that time. We formulate this as a problem of sequential decision making under uncertainty where at a given iteration a planning policy must map the state of the search to a planning action. Unfortunately, the training process for such partial information based policies is slow to converge and susceptible to poor local minima. Our key insight is that if we could fully observe the underlying world map, we would easily be able to disambiguate between good and bad actions. We hence present a novel data-driven imitation learning framework to efficiently train planning policies by imitating a clairvoyant oracle - an oracle that at train time has full knowledge about the world map and can compute optimal decisions. We leverage the fact that for planning problems, such oracles can be efficiently computed and derive performance guarantees for the learnt policy. We examine two important domains that rely on partial information based policies - informative path planning and search based motion planning. We validate the approach on a spectrum of environments for both problem domains, including experiments on a real UAV, and show that the learnt policy consistently outperforms state-of-the-art algorithms.

Includes members:
  Member "approximation algorithms for optimal decision trees and adaptive tsp problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a tight analysis of greedy yields subexponential time approximation for uniform decision tree")
  Member "deep reinforcement learning a brief survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "deeply aggrevated differentiable imitation learning for sequential prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploration efficient deep reinforcement learning with demonstration guidance for robot control")
  Member "learning sampling distributions for robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "accelerating cooperative planning for automated vehicles with learned heuristics and monte carlo tree search")
  Member "near optimal edge evaluation in explicit generalized binomial graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "posterior sampling for anytime motion planning on graphs with expensive to evaluate edges")
  Member "qmdp net deep learning for planning under partial observability":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")
  Member "learning from the hindsight plan episodic mpc improvement":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "information theoretic model predictive q learning")
  Member "deep reinforcement learning for dialogue generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

Question: Please tell me which class the source paper 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)