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: "accurate contact localization and indentation depth prediction with an optics based tactile sensor"
  Abstract: Traditional methods to achieve high localization accuracy with tactile sensors usually use a matrix of miniaturized individual sensors distributed on the area of interest. This approach usually comes at a price of increased complexity in fabrication and circuitry, and can be hard to adapt for non planar geometries. We propose to use low cost optic components mounted on the edges of the sensing area to measure how light traveling through an elastomer is affected by touch. Multiple light emitters and receivers provide us with a rich signal set that contains the necessary information to pinpoint both the location and depth of an indentation with high accuracy. We demonstrate sub-millimeter accuracy on location and depth on a 20mm by 20mm active sensing area. Our sensor provides high depth sensitivity as a result of two different modalities in how light is guided through our elastomer. This method results in a low cost, easy to manufacture sensor. We believe this approach can be adapted to cover non-planar surfaces, simplifying future integration in robot skin applications.

Connected through hyperedge HE-1 (induced by source paper "omnitact a multi directional high resolution touch sensor"):
  with members:
    - "manipulation by feel touch based control with deep predictive models"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "improved gelsight tactile sensor for measuring geometry and slip"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "improved proximity contact and force sensing via optimization of elastomer air interface geometry"):
  with members:
    - "improving grasp performance using in hand proximity and contact sensing"

Connected through hyperedge HE-3 (induced by source paper "data driven super resolution on a tactile dome"):
  with members:
    - "contact localization through spatially overlapping piezoresistive signals"

Connected through hyperedge HE-4 (induced by source paper "touch sensors with overlapping signals concept investigation on planar sensors with resistive or optical transduction"):
  with members:
    - "contact localization through spatially overlapping piezoresistive signals"

Connected through hyperedge HE-5 (induced by source paper "data driven tactile sensing using spatially overlapping signals"):
  with members:
    - "contact localization through spatially overlapping piezoresistive signals"

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)