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 "value iteration networks on multiple levels of abstraction"):
  Abstract: Learning-based methods are promising to plan robot motion without performing extensive search, which is needed by many non-learning approaches. Recently, Value Iteration Networks (VINs) received much interest since---in contrast to standard CNN-based architectures---they learn goal-directed behaviors which generalize well to unseen domains. However, VINs are restricted to small and low-dimensional domains, limiting their applicability to real-world planning problems. #R##N#To address this issue, we propose to extend VINs to representations with multiple levels of abstraction. While the vicinity of the robot is represented in sufficient detail, the representation gets spatially coarser with increasing distance from the robot. The information loss caused by the decreasing resolution is compensated by increasing the number of features representing a cell. We show that our approach is capable of solving significantly larger 2D grid world planning tasks than the original VIN implementation. In contrast to a multiresolution coarse-to-fine VIN implementation which does not employ additional descriptive features, our approach is capable of solving challenging environments, which demonstrates that the proposed method learns to encode useful information in the additional features. As an application for solving real-world planning tasks, we successfully employ our method to plan omnidirectional driving for a search-and-rescue robot in cluttered terrain.

Includes members:
  Member "towards learning abstract representations for locomotion planning in high dimensional state spaces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mapping state space using landmarks for universal goal reaching")
  Member "rgb d object detection and semantic segmentation for autonomous manipulation in clutter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands")
  Member "planning hybrid driving stepping locomotion on multiple levels of abstraction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "remote mobile manipulation with the centauro robot full body telepresence and autonomous operator assistance")
  Member "gated path planning networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep compositional robotic planners that follow natural language commands")
  Member "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "refining 6d object pose predictions using abstract render and compare")
  Member "learning generalized reactive policies using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transferable task execution from pixels through deep planning domain learning")
  Member "generalized value iteration networks life beyond lattices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "asnets deep learning for generalised planning")
  Member "anytime hybrid driving stepping locomotion planning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "remote mobile manipulation with the centauro robot full body telepresence and autonomous operator assistance")

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)