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: "learning shape abstractions by assembling volumetric primitives"
  Abstract: We present a learning framework for abstracting complex shapes by learning to assemble objects using 3D volumetric primitives. In addition to generating simple and geometrically interpretable explanations of 3D objects, our framework also allows us to automatically discover and exploit consistent structure in the data. We demonstrate that using our method allows predicting shape representations which can be leveraged for obtaining a consistent parsing across the instances of a shape collection and constructing an interpretable shape similarity measure. We also examine applications for image-based prediction as well as shape manipulation.

Connected through hyperedge HE-1 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "pq net a generative part seq2seq network for 3d shapes"
    - "object driven text to image synthesis via adversarial training"
    - "cvxnets learnable convex decomposition"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"

Connected through hyperedge HE-2 (induced by source paper "total3dunderstanding joint layout object pose and mesh reconstruction for indoor scenes from a single image"):
  with members:
    - "3d scene reconstruction with multi layer depth and epipolar transformers"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning elementary structures for 3d shape generation and matching"
    - "holistic scene understanding single view 3d holistic scene parsing and human pose estimation with human object interaction and physical commonsense"
    - "learning to infer and execute 3d shape programs"
    - "a skeleton bridged deep learning approach for generating meshes of complex topologies from single rgb images"
    - "few shot generalization for single image 3d reconstruction via priors"
    - "deep level sets implicit surface representations for 3d shape inference"

Connected through hyperedge HE-3 (induced by source paper "std net structure preserving and topology adaptive deformation network for 3d reconstruction from a single image"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "3dn 3d deformation network"
    - "sdm net deep generative network for structured deformable mesh"
    - "learning category specific mesh reconstruction from image collections"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "3d coded 3d correspondences by deep deformation"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-4 (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-5 (induced by source paper "unsupervised program synthesis for images using tree structured lstm"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "learning to infer and execute 3d shape programs"
    - "write execute assess program synthesis with a repl"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "3d aware scene manipulation via inverse graphics"
    - "a tree based decoder for neural machine translation"
    - "automatic program synthesis of long programs with a learned garbage collector"

Connected through hyperedge HE-6 (induced by source paper "segmentation and recovery of superquadric models using convolutional neural networks"):
  with members:
    - "recovery of superquadrics from range images using deep learning a preliminary study"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "mask r cnn"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-7 (induced by source paper "something else compositional action recognition with spatial temporal interaction networks"):
  with members:
    - "spatio temporal action graph networks"
    - "meta learning with differentiable convex optimization"
    - "a closer look at few shot classification"
    - "few shot video classification via temporal alignment"
    - "reasoning about human object interactions through dual attention networks"
    - "long term feature banks for detailed video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "actor centric relation network"

Connected through hyperedge HE-8 (induced by source paper "mesh r cnn"):
  with members:
    - "learning to infer and execute 3d shape programs"
    - "learning category specific mesh reconstruction from image collections"
    - "splatnet sparse lattice networks for point cloud processing"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "tangent convolutions for dense prediction in 3d"
    - "occupancy networks learning 3d reconstruction in function space"

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)