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 "towards learning to detect and predict contact events on vision based tactile sensors"):
  Abstract: In essence, successful grasp boils down to correct responses to multiple contact events between fingertips and objects. In most scenarios, tactile sensing is adequate to distinguish contact events. Due to the nature of high dimensionality of tactile information, classifying spatiotemporal tactile signals using conventional model-based methods is difficult. In this work, we propose to predict and classify tactile signal using deep learning methods, seeking to enhance the adaptability of the robotic grasp system to external event changes that may lead to grasping failure. We develop a deep learning framework and collect 6650 tactile image sequences with a vision-based tactile sensor, and the neural network is integrated into a contact-event-based robotic grasping system. In grasping experiments, we achieved 52% increase in terms of object lifting success rate with contact detection, significantly higher robustness under unexpected loads with slip prediction compared with open-loop grasps, demonstrating that integration of the proposed framework into robotic grasping system substantially improves picking success rate and capability to withstand external disturbances.

Includes members:
  Member "maintaining grasps within slipping bound by monitoring incipient slip":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tactile dexterity manipulation primitives with tactile feedback")
  Member "slip detection with combined tactile and visual information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tactile based insertion for dense box packing")
  Member "dense tactile force distribution estimation using gelslim and inverse fem":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tactile based insertion for dense box packing")
  Member "fingervision tactile sensor design and slip detection using convolutional lstm network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tactile based insertion for dense box packing")
  Member "slip detection analysis and calibration of univariate tactile signals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "effective estimation of contact force and torque for vision based tactile sensor with helmholtz hodge decomposition")
  Member "the feeling of success does touch sensing help predict grasp outcomes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data")
  Member "unsupervised learning for physical interaction through video prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "deep predictive coding networks for video prediction and unsupervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "videoflow a conditional flow based model for stochastic video generation")

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)