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: "shape independent hardness estimation using deep learning and a gelsight tactile sensor"
  Abstract: Hardness is among the most important attributes of an object that humans learn about through touch. However, approaches for robots to estimate hardness are limited, due to the lack of information provided by current tactile sensors. In this work, we address these limitations by introducing a novel method for hardness estimation, based on the GelSight tactile sensor, and the method does not require accurate control of contact conditions or the shape of objects. A GelSight has a soft contact interface, and provides high resolution tactile images of contact geometry, as well as contact force and slip conditions. In this paper, we try to use the sensor to measure hardness of objects with multiple shapes, under a loosely controlled contact condition. The contact is made manually or by a robot hand, while the force and trajectory are unknown and uneven. We analyze the data using a deep constitutional (and recurrent) neural network. Experiments show that the neural net model can estimate the hardness of objects with different shapes and hardness ranging from 8 to 87 in Shore 00 scale.

Connected through hyperedge HE-1 (induced by source paper "scalable tactile sensing for an omni adaptive soft robot finger"):
  with members:
    - "real time soft robot 3d proprioception via deep vision based sensing"

Connected through hyperedge HE-2 (induced by source paper "tactile based insertion for dense box packing"):
  with members:
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "realtime state estimation with tactile and visual sensing for inserting a suction held object"
    - "slip detection with combined tactile and visual information"
    - "dense tactile force distribution estimation using gelslim and inverse fem"
    - "fingervision tactile sensor design and slip detection using convolutional lstm network"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-3 (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"
    - "learning to grasp without seeing"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "tactile mapping and localization from high resolution tactile imprints"):
  with members:
    - "learning dexterous in hand manipulation"
    - "3d shape perception from monocular vision touch and shape priors"
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "iclap shape recognition by combining proprioception and touch sensing"
    - "dense tactile force distribution estimation using gelslim and inverse fem"
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "robotic tactile perception of object properties a review"
    - "active end effector pose selection for tactile object recognition through monte carlo tree search"

Connected through hyperedge HE-5 (induced by source paper "manipulation by feel touch based control with deep predictive models"):
  with members:
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning latent space dynamics for tactile servoing"
    - "stochastic adversarial video prediction"
    - "self supervised visual planning with temporal skip connections"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "deep visual foresight for planning robot motion"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-6 (induced by source paper "learning an action conditional model for haptic texture generation"):
  with members:
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "interactive perception leveraging action in perception and perception in action"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "shear invariant sliding contact perception with a soft tactile sensor"):
  with members:
    - "voronoi features for tactile sensing direct inference of pressure shear and contact locations"
    - "principal components of touch"

Connected through hyperedge HE-8 (induced by source paper "learning to grasp without seeing"):
  with members:
    - "semi supervised haptic material recognition for robots using generative adversarial networks"
    - "learning to represent haptic feedback for partially observable tasks"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "cassl curriculum accelerated self supervised learning"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "analysis and observations from the first amazon picking challenge"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "embed to control a locally linear latent dynamics model for control from raw images"

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)