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: "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

Connected through hyperedge HE-1 (induced by source paper "tactile dexterity manipulation primitives with tactile feedback"):
  with members:
    - "manipulation by feel touch based control with deep predictive models"
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "in hand object stabilization by independent finger control"
    - "in hand manipulation via motion cones"
    - "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"
    - "reactive planar manipulation with convex hybrid mpc"
    - "analysis and observations from the first amazon picking challenge"

Connected through hyperedge HE-2 (induced by source paper "mupnet multi modal predictive coding network for place recognition by unsupervised learning of joint visuo tactile latent representations"):
  with members:
    - "vita slam a bio inspired visuo tactile slam for navigation while interacting with aliased environments"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "convolutional neural network based place recognition"

Connected through hyperedge HE-3 (induced by source paper "dense tactile force distribution estimation using gelslim and inverse fem"):
  with members:
    - "improved gelsight tactile sensor for measuring geometry and slip"

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)