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: "deep learning for tactile understanding from visual and haptic data"
  Abstract: Robots which interact with the physical world will benefit from a fine-grained tactile understanding of objects and surfaces. Additionally, for certain tasks, robots may need to know the haptic properties of an object before touching it. To enable better tactile understanding for robots, we propose a method of classifying surfaces with haptic adjectives (e.g., compressible or smooth) from both visual and physical interaction data. Humans typically combine visual predictions and feedback from physical interactions to accurately predict haptic properties and interact with the world. Inspired by this cognitive pattern, we propose and explore a purely visual haptic prediction model. Purely visual models enable a robot to "feel" without physical interaction. Furthermore, we demonstrate that using both visual and physical interaction signals together yields more accurate haptic classification. Our models take advantage of recent advances in deep neural networks by employing a unified approach to learning features for physical interaction and visual observations. Even though we employ little domain specific knowledge, our model still achieves better results than methods based on hand-designed features.

Connected through hyperedge HE-1 (induced by source paper "optimal deep learning for robot touch"):
  with members:
    - "manipulation by feel touch based control with deep predictive models"
    - "learning to identify object instances by touch tactile recognition via multimodal matching"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "from pixels to percepts highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor"
    - "vitac feature sharing between vision and tactile sensing for cloth texture recognition"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "gaining a sense of touch physical parameters estimation using a soft gripper and neural networks"):
  with members:
    - "inertial sensor based humanoid joint state estimation"

Connected through hyperedge HE-3 (induced by source paper "making sense of vision and touch learning multimodal representations for contact rich tasks"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "joint inference of kinematic and force trajectories with visuo tactile sensing"
    - "from variational to deterministic autoencoders"
    - "manipulation by feel touch based control with deep predictive models"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning latent space dynamics for tactile servoing"

Connected through hyperedge HE-4 (induced by source paper "towards tactile internet in beyond 5g era recent advances current issues and future directions"):
  with members:
    - "emerging edge computing technologies for distributed iot systems"
    - "the effect of iot new features on security and privacy new threats existing solutions and challenges yet to be solved"
    - "ultrareliable and low latency communication techniques for tactile internet services"
    - "scalable intelligence enabled networking with traffic engineering in 5g scenarios for future audio visual tactile internet"
    - "distributed optimization for energy efficient fog computing in the tactile internet"
    - "joint uplink and downlink resource configuration for ultra reliable and low latency communications"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "robust multi path communications for uavs in the urban iot"

Connected through hyperedge HE-5 (induced by source paper "deep robotic prediction with hierarchical rgb d fusion"):
  with members:
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "learning robotic assembly from cad"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "learning to represent haptic feedback for partially observable tasks"
    - "grasp pose detection in point clouds"

Connected through hyperedge HE-6 (induced by source paper "deep gated multi modal learning in hand object pose changes estimation using tactile and image data"):
  with members:
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "bop benchmark for 6d object pose estimation"
    - "learning dexterous in hand manipulation"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "segmentation driven 6d object pose estimation"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "robust deep multi modal learning based on gated information fusion network"
    - "interactively picking real world objects with unconstrained spoken language instructions"

Connected through hyperedge HE-7 (induced by source paper "improving grounded natural language understanding through human robot dialog"):
  with members:
    - "teaching robots to imitate a human with no on teacher sensors what are the key challenges"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "visual curiosity learning to ask questions to learn visual recognition"
    - "neural amr sequence to sequence models for parsing and generation"
    - "learning interpretable spatial operations in a rich 3d blocks world"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "language to logical form with neural attention"

Connected through hyperedge HE-8 (induced by source paper "touching to see and seeing to feel robotic cross modal sensorydata generation for visual tactile perception"):
  with members:
    - "iclap shape recognition by combining proprioception and touch sensing"
    - "vitac feature sharing between vision and tactile sensing for cloth texture recognition"
    - "domain adaptation for visual applications a comprehensive survey"
    - "robotic tactile perception of object properties a review"
    - "iterative closest labeled point for tactile object shape recognition"
    - "generative adversarial text to image synthesis"
    - "image to image translation with conditional adversarial networks"
    - "context encoders feature learning by inpainting"

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)