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: "anytime hybrid driving stepping locomotion planning"
  Abstract: Hybrid driving-stepping locomotion is an effective approach for navigating in a variety of environments. Long, sufficiently even distances can be quickly covered by driving while obstacles can be overcome by stepping. Our quadruped robot Momaro, with steerable pairs of wheels located at the end of each of its compliant legs, allows such locomotion. Planning respective paths attracted only little attention so far. We propose a navigation planning method which generates hybrid locomotion paths. The planner chooses driving mode whenever possible and takes into account the detailed robot footprint. If steps are required, the planner includes those. To accelerate planning, steps are planned first as abstract manoeuvres and are expanded afterwards into detailed motion sequences. Our method ensures at all times that the robot stays stable. Experiments show that the proposed planner is capable of providing paths in feasible time, even for challenging terrain.

Connected through hyperedge HE-1 (induced by source paper "value iteration networks on multiple levels of abstraction"):
  with members:
    - "towards learning abstract representations for locomotion planning in high dimensional state spaces"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "planning hybrid driving stepping locomotion on multiple levels of abstraction"
    - "gated path planning networks"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "learning generalized reactive policies using deep neural networks"
    - "generalized value iteration networks life beyond lattices"
    - "qmdp net deep learning for planning under partial observability"

Connected through hyperedge HE-2 (induced by source paper "remote mobile manipulation with the centauro robot full body telepresence and autonomous operator assistance"):
  with members:
    - "nimbro rescue solving disaster response tasks through mobile manipulation robot momaro"
    - "planning hybrid driving stepping locomotion on multiple levels of abstraction"
    - "transferring category based functional grasping skills by latent space non rigid registration"
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "autonomous dual arm manipulation of familiar objects"
    - "transferring grasping skills to novel instances by latent space non rigid registration"
    - "point set registration coherent point drift"

Connected through hyperedge HE-3 (induced by source paper "towards learning abstract representations for locomotion planning in high dimensional state spaces"):
  with members:
    - "universal planning networks"
    - "planning hybrid driving stepping locomotion on multiple levels of abstraction"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "qmdp net deep learning for planning under partial observability"
    - "end to end learning for self driving cars"
    - "value iteration networks"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-4 (induced by source paper "flexible disaster response of tomorrow final presentation and evaluation of the centauro system"):
  with members:
    - "towards learning abstract representations for locomotion planning in high dimensional state spaces"
    - "transferring category based functional grasping skills by latent space non rigid registration"
    - "autonomous dual arm manipulation of familiar objects"
    - "transferring grasping skills to novel instances by latent space non rigid registration"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-5 (induced by source paper "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"):
  with members:
    - "planning hybrid driving stepping locomotion on multiple levels of abstraction"
    - "transferring category based functional grasping skills by latent space non rigid registration"
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"
    - "transferring grasping skills to novel instances by latent space non rigid registration"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "point set registration coherent point drift"

Connected through hyperedge HE-6 (induced by source paper "keep rollin whole body motion control and planning for wheeled quadrupedal robots"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "efficient kinematic planning for mobile manipulators with non holonomic constraints using optimal control"

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)