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: "autonomous dual arm manipulation of familiar objects"
  Abstract: Autonomous dual-arm manipulation is an essential skill to deploy robots in unstructured scenarios. However, this is a challenging undertaking, particularly in terms of perception and planning. Unstructured scenarios are full of objects with different shapes and appearances that have to be grasped in a very specific manner so they can be functionally used. In this paper we present an integrated approach to perform dual-arm pick tasks autonomously. Our method consists of semantic segmentation, object pose estimation, deformable model registration, grasp planning and arm trajectory optimization. The entire pipeline can be executed on-board and is suitable for on-line grasping scenarios. For this, our approach makes use of accumulated knowledge expressed as convolutional neural network models and low-dimensional latent shape spaces. For manipulating objects, we propose a stochastic trajectory optimization that includes a kinematic chain closure constraint. Evaluation in simulation and on the real robot corroborates the feasibility and applicability of the proposed methods on a task of picking up unknown watering cans and drills using both arms.

Connected through hyperedge HE-1 (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"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "learning postural synergies for categorical grasping through shape space registration"
    - "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-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"
    - "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-3 (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"
    - "transferring grasping skills to novel instances by latent space non rigid registration"
    - "anytime hybrid driving stepping locomotion planning"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-4 (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"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "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"

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)