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: "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
  Abstract: Mobile manipulation tasks are one of the key challenges in the field of search and rescue (SAR) robotics requiring robots with flexible locomotion and manipulation abilities. Since the tasks are mostly unknown in advance, the robot has to adapt to a wide variety of terrains and workspaces during a mission. The centaur-like robot Centauro has a hybrid legged-wheeled base and an anthropomorphic upper body to carry out complex tasks in environments too dangerous for humans. Due to its high number of degrees of freedom, controlling the robot with direct teleoperation approaches is challenging and exhausting. Supervised autonomy approaches are promising to increase quality and speed of control while keeping the flexibility to solve unknown tasks. We developed a set of operator assistance functionalities with different levels of autonomy to control the robot for challenging locomotion and manipulation tasks. The integrated system was evaluated in disaster response scenarios and showed promising performance.

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"
    - "learning generalized reactive policies using deep neural networks"
    - "generalized value iteration networks life beyond lattices"
    - "anytime hybrid driving stepping locomotion planning"
    - "qmdp net deep learning for planning under partial observability"

Connected through hyperedge HE-2 (induced by source paper "refining 6d object pose predictions using abstract render and compare"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "light weight refinenet for real time semantic segmentation"
    - "what do deep networks like to see"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "neural 3d mesh renderer"

Connected through hyperedge HE-3 (induced by source paper "autonomous bimanual functional regrasping of novel object class instances"):
  with members:
    - "dual arm in hand manipulation and regrasping using dexterous manipulation graphs"
    - "transferring category based functional grasping skills by latent space non rigid registration"
    - "light weight refinenet for real time semantic segmentation"
    - "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation"
    - "learning postural synergies for categorical grasping through shape space registration"
    - "autonomous dual arm manipulation of familiar objects"
    - "transferring grasping skills to novel instances by latent space non rigid registration"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

Connected through hyperedge HE-4 (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"
    - "autonomous dual arm manipulation of familiar objects"
    - "transferring grasping skills to novel instances by latent space non rigid registration"
    - "anytime hybrid driving stepping locomotion planning"
    - "point set registration coherent point drift"

Connected through hyperedge HE-5 (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"
    - "anytime hybrid driving stepping locomotion planning"
    - "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-6 (induced by source paper "a vr system for immersive teleoperation and live exploration with a mobile robot"):
  with members:
    - "slamcast large scale real time 3d reconstruction and streaming for immersive multi client live telepresence"
    - "team nimbro at mbzirc 2017 autonomous valve stem turning using a wrench"
    - "baxter s homunculus virtual reality spaces for teleoperation in manufacturing"

Connected through hyperedge HE-7 (induced by source paper "learning postural synergies for categorical grasping through shape space registration"):
  with members:
    - "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"
    - "compact part based shape spaces for dense correspondences"
    - "point set registration coherent point drift"

Connected through hyperedge HE-8 (induced by source paper "autonomous dual arm manipulation of familiar objects"):
  with members:
    - "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"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)