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 "tactile mapping and localization from high resolution tactile imprints"):
  Abstract: This work studies the problem of shape reconstruction and object localization using a vision-based tactile sensor, GelSlim. The main contributions are the recovery of local shapes from contact, an approach to reconstruct the tactile shape of objects from tactile imprints, and an accurate method for object localization of previously reconstructed objects. The algorithms can be applied to a large variety of 3D objects and provide accurate tactile feedback for in-hand manipulation. Results show that by exploiting the dense tactile information we can reconstruct the shape of objects with high accuracy and do on-line object identification and localization, opening the door to reactive manipulation guided by tactile sensing. We provide videos and supplemental information in the project's website this http URL

Includes members:
  Member "learning dexterous in hand manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "never give up learning directed exploration strategies")
  Member "3d shape perception from monocular vision touch and shape priors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "data efficient learning for sim to real robotic grasping using deep point cloud prediction networks")
  Member "maintaining grasps within slipping bound by monitoring incipient slip":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tactile dexterity manipulation primitives with tactile feedback")
  Member "iclap shape recognition by combining proprioception and touch sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "touching to see and seeing to feel robotic cross modal sensorydata generation for visual tactile perception")
  Member "dense tactile force distribution estimation using gelslim and inverse fem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tactile based insertion for dense box packing")
  Member "tactile regrasp grasp adjustments via simulated tactile transformations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reaching grasping and re grasping learning multimode grasping skills")
  Member "robotic tactile perception of object properties a review":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "active end effector pose selection for tactile object recognition through monte carlo tree search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "action for better prediction")

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)