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 "autonomous bimanual functional regrasping of novel object class instances"):
  Abstract: In human-made scenarios, robots need to be able to fully operate objects in their surroundings, i.e., objects are required to be functionally grasped rather than only picked. This imposes very strict constraints on the object pose such that a direct grasp can be performed. Inspired by the anthropomorphic nature of humanoid robots, we propose an approach that first grasps an object with one hand, obtaining full control over its pose, and performs the functional grasp with the second hand subsequently. Thus, we develop a fully autonomous pipeline for dual-arm functional regrasping of novel familiar objects, i.e., objects never seen before that belong to a known object category, e.g., spray bottles. This process involves semantic segmentation, object pose estimation, non-rigid mesh registration, grasp sampling, handover pose generation and in-hand pose refinement. The latter is used to compensate for the unpredictable object movement during the first grasp. The approach is applied to a human-like upper body. To the best knowledge of the authors, this is the first system that exhibits autonomous bimanual functional regrasping capabilities. We demonstrate that our system yields reliable success rates and can be applied on-line to real-world tasks using only one off-the-shelf RGB-D sensor.

Includes members:
  Member "dual arm in hand manipulation and regrasping using dexterous manipulation graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning hierarchical control for robust in hand manipulation")
  Member "transferring category based functional grasping skills by latent space non rigid registration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "remote mobile manipulation with the centauro robot full body telepresence and autonomous operator assistance")
  Member "light weight refinenet for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nas count counting by density with neural architecture search")
  Member "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "benchmarking in hand manipulation")
  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 "value iteration networks on multiple levels of abstraction")
  Member "learning postural synergies for categorical grasping through shape space registration":
    also connected through other hyperedges:
      (none)
  Member "autonomous dual arm manipulation of familiar objects":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "remote mobile manipulation with the centauro robot full body telepresence and autonomous operator assistance")
  Member "transferring grasping skills to novel instances by latent space non rigid registration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "kpam keypoint affordances for category level robotic manipulation")

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)