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: "3d shape perception from monocular vision touch and shape priors"
  Abstract: Perceiving accurate 3D object shape is important for robots to interact with the physical world. Current research along this direction has been primarily relying on visual observations. Vision, however useful, has inherent limitations due to occlusions and the 2D-3D ambiguities, especially for perception with a monocular camera. In contrast, touch gets precise local shape information, though its efficiency for reconstructing the entire shape could be low. In this paper, we propose a novel paradigm that efficiently perceives accurate 3D object shape by incorporating visual and tactile observations, as well as prior knowledge of common object shapes learned from large-scale shape repositories. We use vision first, applying neural networks with learned shape priors to predict an object's 3D shape from a single-view color image. We then use tactile sensing to refine the shape; the robot actively touches the object regions where the visual prediction has high uncertainty. Our method efficiently builds the 3D shape of common objects from a color image and a small number of tactile explorations (around 10). Our setup is easy to apply and has potentials to help robots better perform grasping or manipulation tasks on real-world objects.

Connected through hyperedge HE-1 (induced by source paper "data efficient learning for sim to real robotic grasping using deep point cloud prediction networks"):
  with members:
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "grasp2vec learning object representations from self supervised grasping"
    - "multiresolution tree networks for 3d point cloud processing"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "parsing geometry using structure aware shape templates"

Connected through hyperedge HE-2 (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"
    - "learning to grasp without seeing"
    - "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-3 (induced by source paper "tactile mapping and localization from high resolution tactile imprints"):
  with members:
    - "learning dexterous in hand manipulation"
    - "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"
    - "shape independent hardness estimation using deep learning and a gelsight tactile sensor"

Connected through hyperedge HE-4 (induced by source paper "visual tactile fusion object clustering"):
  with members:
    - "representative task self selection for flexible clustered lifelong learning"
    - "semantic transferable weakly supervised endoscopic lesions segmentation"
    - "lifelong spectral clustering"
    - "deep spectral clustering using dual autoencoder network"
    - "connecting touch and vision via cross modal prediction"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "active clothing material perception using tactile sensing and deep learning"
    - "deep learning for tactile understanding from visual and haptic data"

Connected through hyperedge HE-5 (induced by source paper "learning shape priors for single view 3d completion and reconstruction"):
  with members:
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "octnetfusion learning depth fusion from data"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "hierarchical surface prediction for 3d object reconstruction"
    - "improved training of wasserstein gans"
    - "weakly supervised 3d reconstruction with adversarial constraint"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"

Connected through hyperedge HE-6 (induced by source paper "multi modal geometric learning for grasping and manipulation"):
  with members:
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "a note on the triangle inequality for the jaccard distance"
    - "shape completion enabled robotic grasping"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

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.CV(Computer Vision and Pattern Recognition)