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 "more than a feeling learning to grasp and regrasp using vision and touch"):
  Abstract: For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus cannot easily benefit from feedback after initiating contact. In this letter, we investigate how a robot can learn to use tactile information to iteratively and efficiently adjust its grasp. To this end, we propose an end-to-end action-conditional model that learns regrasping policies from raw visuo-tactile data. This model - a deep, multimodal convolutional network - predicts the outcome of a candidate grasp adjustment, and then executes a grasp by iteratively selecting the most promising actions. Our approach requires neither calibration of the tactile sensors nor any analytical modeling of contact forces, thus reducing the engineering effort required to obtain efficient grasping policies. We train our model with data from about 6450 grasping trials on a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger. Across extensive experiments, our approach outperforms a variety of baselines at 1) estimating grasp adjustment outcomes, 2) selecting efficient grasp adjustments for quick grasping, and 3) reducing the amount of force applied at the fingers, while maintaining competitive performance. Finally, we study the choices made by our model and show that it has successfully acquired useful and interpretable grasping behaviors.

Includes members:
  Member "learning to grasp without seeing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mat multi fingered adaptive tactile grasping via deep reinforcement learning")
  Member "tactile regrasp grasp adjustments via simulated tactile transformations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reaching grasping and re grasping learning multimode grasping skills")
  Member "connecting look and feel associating the visual and tactile properties of physical materials":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal deep learning for robot touch")
  Member "the feeling of success does touch sensing help predict grasp outcomes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data")
  Member "estimating tactile data for adaptive grasping of novel objects":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mat multi fingered adaptive tactile grasping via deep reinforcement learning")
  Member "improved gelsight tactile sensor for measuring geometry and slip":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "omnitact a multi directional high resolution touch sensor")
  Member "shape independent hardness estimation using deep learning and a gelsight tactile sensor":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scalable tactile sensing for an omni adaptive soft robot finger")
  Member "deep learning a grasp function for grasping under gripper pose uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")

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)