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: "gelslim a high resolution compact robust and calibrated tactile sensing finger"
  Abstract: This work describes the development of a high-resolution tactile-sensing finger for robot grasping. This finger, inspired by previous GelSight sensing techniques, features an integration that is slimer, more robust, and with more homogenoeus output than previous vision-based tactile sensors. To achieve a compact integration, we redesign the optical path from illumination source to camera by combining light guides and an arrangement of mirror reflections. The optical path can be parametrized with geometric design variables and we describe the tradeoffs between the thickness of the finger, the depth of field of the camera, and the size of the tactile sensing pad. The sensor can sustain the wear from continuous use--and abuse--in grasping tasks by combining tougher materials for the compliant soft gel, a textured fabric skin, a structurally rigid body, and a calibration process that ensures homogeneous illumination and contrast of the tactile images during use. Finally, we evaluate the sensor's durability along four metrics that capture the signal quality during more than 3000 grasping experiments.

Connected through hyperedge HE-1 (induced by source paper "tactile model o fabrication and testing of a 3d printed three fingered tactile robot hand"):
  with members:
    - "learning to identify object instances by touch tactile recognition via multimodal matching"
    - "in hand object stabilization by independent finger control"
    - "from pixels to percepts highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "improved gelsight tactile sensor for measuring geometry and slip"
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "contact aware controller design for complementarity systems"):
  with members:
    - "a quasi static model and simulation approach for pushing grasping and jamming"
    - "modeling and analysis of non unique behaviors in multiple frictional impacts"
    - "manipulation by feel touch based control with deep predictive models"
    - "leveraging contact forces for learning to grasp"
    - "lvis learning from value function intervals for contact aware robot controllers"
    - "reactive planar manipulation with convex hybrid mpc"

Connected through hyperedge HE-3 (induced by source paper "exoskeleton covered soft finger with vision based proprioception and exteroception"):
  with members:
    - "real time soft robot 3d proprioception via deep vision based sensing"
    - "semantic image synthesis with spatially adaptive normalization"
    - "bodily aware soft robots integration of proprioceptive and exteroceptive sensors"
    - "improved gelsight tactile sensor for measuring geometry and slip"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "towards vision based robotic skins a data driven multi camera tactile sensor"):
  with members:
    - "exoskeleton covered soft finger with vision based proprioception and exteroception"
    - "ground truth force distribution for learning based tactile sensing a finite element approach"
    - "touch sensors with overlapping signals concept investigation on planar sensors with resistive or optical transduction"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "soft bubble a highly compliant dense geometry tactile sensor for robot manipulation"):
  with members:
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "deep residual learning for image recognition"
    - "reaching in clutter with whole arm tactile sensing"

Connected through hyperedge HE-6 (induced by source paper "from pixels to percepts highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor"):
  with members:
    - "voronoi features for tactile sensing direct inference of pressure shear and contact locations"
    - "the limits and potentials of deep learning for robotics"
    - "principal components of touch"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "slip detection with combined tactile and visual information"
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "the feeling of success does touch sensing help predict grasp outcomes"

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)