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 functional dictionaries learning consistent semantic structures on 3d models from functions"
  Abstract: Various 3D semantic attributes such as segmentation masks, geometric features, keypoints, and materials can be encoded as per-point probe functions on 3D geometries. Given a collection of related 3D shapes, we consider how to jointly analyze such probe functions over different shapes, and how to discover common latent structures using a neural network --- even in the absence of any correspondence information. Our network is trained on point cloud representations of shape geometry and associated semantic functions on that point cloud. These functions express a shared semantic understanding of the shapes but are not coordinated in any way. For example, in a segmentation task, the functions can be indicator functions of arbitrary sets of shape parts, with the particular combination involved not known to the network. Our network is able to produce a small dictionary of basis functions for each shape, a dictionary whose span includes the semantic functions provided for that shape. Even though our shapes have independent discretizations and no functional correspondences are provided, the network is able to generate latent bases, in a consistent order, that reflect the shared semantic structure among the shapes. We demonstrate the effectiveness of our technique in various segmentation and keypoint selection applications.

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning of intrinsic structural representation points"):
  with members:
    - "cosegnet deep co segmentation of 3d shapes with group consistency loss"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "learning elementary structures for 3d shape generation and matching"
    - "unsupervised part based disentangling of object shape and appearance"
    - "supervised fitting of geometric primitives to 3d point clouds"
    - "bae net branched autoencoder for shape co segmentation"
    - "3d point cloud generative adversarial network based on tree structured graph convolutions"
    - "3d point capsule networks"

Connected through hyperedge HE-2 (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"
    - "deep part induction from articulated object pairs"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "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-3 (induced by source paper "keypointnet a large scale 3d keypoint dataset aggregated from numerous human annotations"):
  with members:
    - "spair 71k a large scale benchmark for semantic correspondence"
    - "relation shape convolutional neural network for point cloud analysis"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "pointwise rotation invariant network with adaptive sampling and 3d spherical voxel convolution"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "learning compact geometric features"

Connected through hyperedge HE-4 (induced by source paper "fine grained object semantic understanding from correspondences"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "canonical surface mapping via geometric cycle consistency"
    - "spair 71k a large scale benchmark for semantic correspondence"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "self supervised learning of dense shape correspondence"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "unsupervised deep learning for structured shape matching"

Connected through hyperedge HE-5 (induced by source paper "cosegnet deep co segmentation of 3d shapes with group consistency loss"):
  with members:
    - "deep object co segmentation"
    - "faces as lighting probes via unsupervised deep highlight extraction"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "3d shape segmentation via shape fully convolutional networks"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "tags2parts discovering semantic regions from shape tags"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"

Connected through hyperedge HE-6 (induced by source paper "supervised fitting of geometric primitives to 3d point clouds"):
  with members:
    - "deep part induction from articulated object pairs"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "rethinking task and metrics of instance segmentation on 3d point clouds"):
  with members:
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "recurrent pixel embedding for instance grouping"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "what makes for effective detection proposals"
    - "fully convolutional instance aware semantic segmentation"
    - "instance aware semantic segmentation via multi task network cascades"
    - "facenet a unified embedding for face recognition and clustering"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-8 (induced by source paper "3d point capsule networks"):
  with members:
    - "indoor scene understanding in 2 5 3d for autonomous agents a survey"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "generalized capsule networks with trainable routing procedure"
    - "videocapsulenet a simplified network for action detection"
    - "cappronet deep feature learning via orthogonal projections onto capsule subspaces"
    - "fast capsnet for lung cancer screening"
    - "group equivariant capsule networks"

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)