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: "learning to grasp without seeing"
  Abstract: Can a robot grasp an unknown object without seeing it? In this paper, we present a tactile-sensing based approach to this challenging problem of grasping novel objects without prior knowledge of their location or physical properties. Our key idea is to combine touch based object localization with tactile based re-grasping. To train our learning models, we created a large-scale grasping dataset, including more than 30 RGB frames and over 2.8 million tactile samples from 7800 grasp interactions of 52 objects. To learn a representation of tactile signals, we propose an unsupervised auto-encoding scheme, which shows a significant improvement of 4-9% over prior methods on a variety of tactile perception tasks. Our system consists of two steps. First, our touch localization model sequentially 'touch-scans' the workspace and uses a particle filter to aggregate beliefs from multiple hits of the target. It outputs an estimate of the object's location, from which an initial grasp is established. Next, our re-grasping model learns to progressively improve grasps with tactile feedback based on the learned features. This network learns to estimate grasp stability and predict adjustment for the next grasp. Re-grasping thus is performed iteratively until our model identifies a stable grasp. Finally, we demonstrate extensive experimental results on grasping a large set of novel objects using tactile sensing alone. Furthermore, when applied on top of a vision-based policy, our re-grasping model significantly boosts the overall accuracy by 10.6%. We believe this is the first attempt at learning to grasp with only tactile sensing and without any prior object knowledge.

Connected through hyperedge HE-1 (induced by source paper "mat multi fingered adaptive tactile grasping via deep reinforcement learning"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "pixel attentive policy gradient for multi fingered grasping in cluttered scenes"
    - "modeling grasp type improves learning based grasp planning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "3d shape perception from monocular vision touch and shape priors"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "leveraging contact forces for learning to grasp"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-2 (induced by source paper "learning to identify object instances by touch tactile recognition via multimodal matching"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "vitac feature sharing between vision and tactile sensing for cloth texture recognition"
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "objects that sound"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "look listen and learn"

Connected through hyperedge HE-3 (induced by source paper "grasping using tactile sensing and deep calibration"):
  with members:
    - "real time policy generation and its application to robot grasping"
    - "evaluation of estimation approaches on the quality and robustness of collision warning system"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "interactive perception leveraging action in perception and perception in action"
    - "deep learning for tactile understanding from visual and haptic data"
    - "deep spatial autoencoders for visuomotor learning"

Connected through hyperedge HE-4 (induced by source paper "robot learning in homes improving generalization and reducing dataset bias"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "semi supervised haptic material recognition for robots using generative adversarial networks"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "asymmetric actor critic for image based robot learning"
    - "unsupervised learning for physical interaction through video prediction"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "learning to push by grasping using multiple tasks for effective learning"

Connected through hyperedge HE-5 (induced by source paper "more than a feeling learning to grasp and regrasp using vision and touch"):
  with members:
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "estimating tactile data for adaptive grasping of novel objects"
    - "improved gelsight tactile sensor for measuring geometry and slip"
    - "shape independent hardness estimation using deep learning and a gelsight tactile sensor"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"

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)