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 part induction from articulated object pairs"
  Abstract: Object functionality is often expressed through part articulation - as when the two rigid parts of a scissor pivot against each other to perform the cutting function. Such articulations are often similar across objects within the same functional category. In this paper we explore how the observation of different articulation states provides evidence for part structure and motion of 3D objects. Our method takes as input a pair of unsegmented shapes representing two different articulation states of two functionally related objects, and induces their common parts along with their underlying rigid motion. This is a challenging setting, as we assume no prior shape structure, no prior shape category information, no consistent shape orientation, the articulation states may belong to objects of different geometry, plus we allow inputs to be noisy and partial scans, or point clouds lifted from RGB images. Our method learns a neural network architecture with three modules that respectively propose correspondences, estimate 3D deformation flows, and perform segmentation. To achieve optimal performance, our architecture alternates between correspondence, deformation flow, and segmentation prediction iteratively in an ICP-like fashion. Our results demonstrate that our method significantly outperforms state-of-the-art techniques in the task of discovering articulated parts of objects. In addition, our part induction is object-class agnostic and successfully generalizes to new and unseen objects.

Connected through hyperedge HE-1 (induced by source paper "learning to group a bottom up framework for 3d part discovery in unseen categories"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "deep functional dictionaries learning consistent semantic structures on 3d models from functions"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "3d shape segmentation via shape fully convolutional networks"
    - "recurrent pixel embedding for instance grouping"
    - "3d semantic segmentation with submanifold sparse convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "bae net branched autoencoder for shape co segmentation"):
  with members:
    - "cosegnet deep co segmentation of 3d shapes with group consistency loss"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "3d point capsule networks"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "adversarial complementary learning for weakly supervised object localization"
    - "learning implicit fields for generative shape modeling"

Connected through hyperedge HE-3 (induced by source paper "prnet self supervised learning for partial to partial registration"):
  with members:
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "kpconv flexible and deformable convolution for point clouds"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "deep closest point learning representations for point cloud registration"
    - "rethinking imagenet pre training"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "point convolutional neural networks by extension operators"

Connected through hyperedge HE-4 (induced by source paper "category level articulated object pose estimation"):
  with members:
    - "shape2motion joint analysis of motion parts and attributes from 3d shapes"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "factored pose estimation of articulated objects using efficient nonparametric belief propagation"
    - "densepose dense human pose estimation in the wild"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-5 (induced by source paper "shape2motion joint analysis of motion parts and attributes from 3d shapes"):
  with members:
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "3d shape segmentation with projective convolutional networks"
    - "scene flow estimation a survey"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "data driven shape analysis and processing"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-6 (induced by source paper "supervised fitting of geometric primitives to 3d point clouds"):
  with members:
    - "deep functional dictionaries learning consistent semantic structures on 3d models from functions"
    - "parsing geometry using structure aware shape templates"
    - "pcpnet learning local shape properties from raw point clouds"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "csgnet neural shape parser for constructive solid geometry"
    - "learning shape abstractions by assembling volumetric primitives"
    - "marr revisited 2d 3d alignment via surface normal prediction"

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)