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: "robust learning of tactile force estimation through robot interaction"
  Abstract: Current methods for estimating force from tactile sensor signals are either inaccurate analytic models or task-specific learned models. In this paper, we explore learning a robust model that maps tactile sensor signals to force. We specifically explore learning a mapping for the SynTouch BioTac sensor via neural networks. We propose a voxelized input feature layer for spatial signals and leverage information about the sensor surface to regularize the loss function. To learn a robust tactile force model that transfers across tasks, we generate ground truth data from three different sources: (1) the BioTac rigidly mounted to a force torque sensor, (2) a robot interacting with a ball rigidly attached to a force sensor to collect a wide range of force readings, and (3) through force inference on a planar pushing task by formalizing the mechanics as a system of particles and optimizing over the object motion. A total of 140k samples were collected from the three sources. To study generalization, we evaluate using the learned model to estimate force inside a feedback controller performing grasp stabilization and object placement. We achieve a median angular accuracy of 0.06 radians in predicting force direction~(66% improvement over the current state of the art) and a median magnitude accuracy of 0.06 N (93% improvement) on a test dataset. Our results can be found on this https URL

Connected through hyperedge HE-1 (induced by source paper "in hand object pose tracking via contact feedback and gpu accelerated robotic simulation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "learning joint reconstruction of hands and manipulated objects"
    - "joint inference of kinematic and force trajectories with visuo tactile sensing"
    - "making sense of vision and touch learning multimodal representations for contact rich tasks"
    - "non smooth newton methods for deformable multi body dynamics"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-2 (induced by source paper "learning the sense of touch in simulation a sim to real strategy for vision based tactile sensing"):
  with members:
    - "ground truth force distribution for learning based tactile sensing a finite element approach"
    - "mat multi fingered adaptive tactile grasping via deep reinforcement learning"
    - "tactile based insertion for dense box packing"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "transfer learning for vision based tactile sensing"
    - "fast optical flow using dense inverse search"
    - "u net convolutional networks for biomedical image segmentation"
    - "spatial transformer networks"

Connected through hyperedge HE-3 (induced by source paper "learning continuous 3d reconstructions for geometrically aware grasping"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "generating grasp poses for a high dof gripper using neural networks"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "robust grasp planning over uncertain shape completions"
    - "modeling grasp type improves learning based grasp planning"
    - "learning implicit fields for generative shape modeling"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation"

Connected through hyperedge HE-4 (induced by source paper "dexpilot vision based teleoperation of dexterous robotic hand arm system"):
  with members:
    - "making convolutional networks shift invariant again"
    - "learning joint reconstruction of hands and manipulated objects"
    - "learning latent plans from play"
    - "generalized feedback loop for joint hand object pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "vision based teleoperation of shadow dexterous hand using end to end deep neural network"
    - "neural kinematic networks for unsupervised motion retargetting"

Connected through hyperedge HE-5 (induced by source paper "joint inference of kinematic and force trajectories with visuo tactile sensing"):
  with members:
    - "realtime state estimation with tactile and visual sensing for inserting a suction held object"
    - "deep forward and inverse perceptual models for tracking and prediction"
    - "realtime state estimation with tactile and visual sensing application to planar manipulation"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "feedback control of the pusher slider system a story of hybrid and underactuated contact dynamics"

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)