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: "learning heuristic search via imitation"
  Abstract: Robotic motion planning problems are typically solved by constructing a search tree of valid maneuvers from a start to a goal configuration. Limited onboard computation and real-time planning constraints impose a limit on how large this search tree can grow. Heuristics play a crucial role in such situations by guiding the search towards potentially good directions and consequently minimizing search effort. Moreover, it must infer such directions in an efficient manner using only the information uncovered by the search up until that time. However, state of the art methods do not address the problem of computing a heuristic that explicitly minimizes search effort. In this paper, we do so by training a heuristic policy that maps the partial information from the search to decide which node of the search tree to expand. Unfortunately, naively training such policies leads to slow convergence and poor local minima. We present SaIL, an efficient algorithm that trains heuristic policies by imitating "clairvoyant oracles" - oracles that have full information about the world and demonstrate decisions that minimize search effort. We leverage the fact that such oracles can be efficiently computed using dynamic programming and derive performance guarantees for the learnt heuristic. We validate the approach on a spectrum of environments which show that SaIL consistently outperforms state of the art algorithms. Our approach paves the way forward for learning heuristics that demonstrate an anytime nature - finding feasible solutions quickly and incrementally refining it over time.

Connected through hyperedge HE-1 (induced by source paper "motion planning networks bridging the gap between learning based and classical motion planners"):
  with members:
    - "neural path planning fixed time near optimal path generation via oracle imitation"
    - "composing task agnostic policies with deep reinforcement learning"
    - "composing ensembles of policies with deep reinforcement learning"
    - "robot motion planning in learned latent spaces"
    - "universal planning networks"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "experience replay for continual learning"

Connected through hyperedge HE-2 (induced by source paper "the regretful agent heuristic aided navigation through progress estimation"):
  with members:
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "talk the walk navigating new york city through grounded dialogue"
    - "gibson env real world perception for embodied agents"
    - "speaker follower models for vision and language navigation"
    - "guided feature transformation gft a neural language grounding module for embodied agents"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"

Connected through hyperedge HE-3 (induced by source paper "planning beyond the sensing horizon using a learned context"):
  with members:
    - "building generalizable agents with a realistic and rich 3d environment"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "learning deep generative spatial models for mobile robots"
    - "deepnav learning to navigate large cities"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "iqa visual question answering in interactive environments"
    - "visual semantic planning using deep successor representations"
    - "embodied question answering"

Connected through hyperedge HE-4 (induced by source paper "continuous relaxation of symbolic planner for one shot imitation learning"):
  with members:
    - "one shot learning of multi step tasks from observation via activity localization in auxiliary video"
    - "learning quickly to plan quickly using modular meta learning"
    - "zero shot visual imitation"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "taco learning task decomposition via temporal alignment for control"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "one shot visual imitation learning via meta learning"
    - "neural task programming learning to generalize across hierarchical tasks"

Connected through hyperedge HE-5 (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"
    - "data driven planning via imitation learning"

Connected through hyperedge HE-6 (induced by source paper "fully convolutional search heuristic learning for rapid path planners"):
  with members:
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "cognitive mapping and planning for visual navigation"
    - "watch this scalable cost function learning for path planning in urban environments"
    - "potential functions based sampling heuristic for optimal path planning"
    - "cnn architectures for large scale audio classification"
    - "value iteration networks"
    - "multi scale context aggregation by dilated convolutions"
    - "maximum entropy deep inverse reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "differentiable gaussian process motion planning"):
  with members:
    - "leveraging experience in lazy search"
    - "differentiable mpc for end to end planning and control"
    - "neural ordinary differential equations"
    - "continuous time gaussian process motion planning via probabilistic inference"
    - "optnet differentiable optimization as a layer in neural networks"
    - "prm rl long range robotic navigation tasks by combining reinforcement learning and sampling based planning"
    - "learning to learn by gradient descent by gradient descent"
    - "value iteration networks"

Connected through hyperedge HE-8 (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"
    - "data driven planning via imitation learning"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"

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)