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: "dense tactile force distribution estimation using gelslim and inverse fem"
  Abstract: In this paper, we present a new version of tactile sensor GelSlim 2.0 with the capability to estimate the contact force distribution in real time. The sensor is vision-based and uses an array of markers to track deformations on a gel pad due to contact. A new hardware design makes the sensor more rugged, parametrically adjustable and improves illumination. Leveraging the sensor's increased functionality, we propose to use inverse Finite Element Method (iFEM), a numerical method to reconstruct the contact force distribution based on marker displacements. The sensor is able to provide force distribution of contact with high spatial density. Experiments and comparison with ground truth show that the reconstructed force distribution is physically reasonable with good accuracy.

Connected through hyperedge HE-1 (induced by source paper "tactile based insertion for dense box packing"):
  with members:
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "realtime state estimation with tactile and visual sensing for inserting a suction held object"
    - "slip detection with combined tactile and visual information"
    - "fingervision tactile sensor design and slip detection using convolutional lstm network"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning robot activities from first person human videos using convolutional future regression"

Connected through hyperedge HE-2 (induced by source paper "tactile mapping and localization from high resolution tactile imprints"):
  with members:
    - "learning dexterous in hand manipulation"
    - "3d shape perception from monocular vision touch and shape priors"
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "iclap shape recognition by combining proprioception and touch sensing"
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "robotic tactile perception of object properties a review"
    - "active end effector pose selection for tactile object recognition through monte carlo tree search"
    - "shape independent hardness estimation using deep learning and a gelsight tactile sensor"

Connected through hyperedge HE-3 (induced by source paper "towards learning to detect and predict contact events on vision based tactile sensors"):
  with members:
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "slip detection with combined tactile and visual information"
    - "fingervision tactile sensor design and slip detection using convolutional lstm network"
    - "slip detection analysis and calibration of univariate tactile signals"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "unsupervised learning for physical interaction through video prediction"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "spatial transformer networks"

Connected through hyperedge HE-4 (induced by source paper "ground truth force distribution for learning based tactile sensing a finite element approach"):
  with members:
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "transfer learning for vision based tactile sensing"
    - "touch sensors with overlapping signals concept investigation on planar sensors with resistive or optical transduction"
    - "fast optical flow using dense inverse search"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "non planar frictional surface contacts modeling and application to grasping"):
  with members:
    - "a fast stochastic contact model for planar pushing and grasping theory and experimental validation"
    - "a convex polynomial force motion model for planar sliding identification and application"

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)