human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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

Includes members:
  Member "more than a feeling learning to grasp and regrasp using vision and touch":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reaching grasping and re grasping learning multimode grasping skills")
  Member "learning sensor feedback models from demonstrations via phase modulated neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning generalisable coupling terms for obstacle avoidance via low dimensional geometric descriptors")
  Member "riemannian motion policies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "camera to robot pose estimation from a single image")
  Member "rmpflow a computational graph for automatic motion policy generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "camera to robot pose estimation from a single image")
  Member "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "benchmarking in hand manipulation")
  Member "super convergence very fast training of residual networks using large learning rates":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rethinking the hyperparameters for fine tuning")
  Member "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deep learning for tactile understanding from visual and haptic data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimal deep learning for robot touch")

Question: Please tell me which class the source paper 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)