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: "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.

Connected through hyperedge HE-1 (induced by source paper "bayesian residual policy optimization scalable bayesian reinforcement learning with clairvoyant experts"):
  with members:
    - "meta learning of sequential strategies"
    - "lets drive driving in a crowd by learning from tree search"
    - "meta learners learning dynamics are unlike learners"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "exploration by random network distillation"
    - "residual policy learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-2 (induced by source paper "sample complexity of probabilistic roadmaps via e nets"):
  with members:
    - "drrt scalable and informed asymptotically optimal multi robot motion planning"
    - "toward asymptotically optimal inspection planning via efficient near optimal graph search"
    - "robot motion planning in learned latent spaces"
    - "fast anytime motion planning for prehensile manipulation in clutter"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "learning sampling distributions for robot motion planning"
    - "the critical radius in sampling based motion planning"
    - "new perspective on sampling based motion planning via random geometric graphs"

Connected through hyperedge HE-3 (induced by source paper "leveraging experience in lazy search"):
  with members:
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "fast policy learning through imitation and reinforcement"
    - "the provable virtue of laziness in motion planning"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "bayesian active edge evaluation on expensive graphs"
    - "near optimal edge evaluation in explicit generalized binomial graphs"
    - "learning heuristic search via imitation"

Connected through hyperedge HE-4 (induced by source paper "neural collision clearance estimator for fast robot motion planning"):
  with members:
    - "learning based proxy collision detection for robot motion planning applications"
    - "estimating configuration space belief from collision checks for motion planning"
    - "robot motion planning in learned latent spaces"
    - "fastron an online learning based model and active learning strategy for proxy collision detection"
    - "learning sampling distributions for robot motion planning"
    - "adam a method for stochastic optimization"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-5 (induced by source paper "a novel approach to model exploration for value function learning"):
  with members:
    - "search based optimal motion planning for automated driving"
    - "learning implicit sampling distributions for motion planning"
    - "self normalizing neural networks"
    - "learning sampling distributions for robot motion planning"
    - "value iteration networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-6 (induced by source paper "learning a lattice planner control set for autonomous vehicles"):
  with members:
    - "an lstm network for highway trajectory prediction"
    - "learning sampling distributions for robot motion planning"
    - "learning heuristic search via imitation"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"

Connected through hyperedge HE-7 (induced by source paper "vision based navigation with language based assistance via imitation learning with indirect intervention"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "virtualhome simulating household activities via programs"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "textworld a learning environment for text based games"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "gibson env real world perception for embodied agents"

Connected through hyperedge HE-8 (induced by source paper "adversarial exploration strategy for self supervised imitation learning"):
  with members:
    - "zero shot visual imitation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "parameter space noise for exploration"
    - "hindsight experience replay"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "one shot visual imitation learning via meta learning"
    - "robust adversarial reinforcement learning"

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)