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: "cvxnets learnable convex decomposition"
  Abstract: Any solid object can be decomposed into a collection of convex polytopes (in short, convexes). When a small number of convexes are used, such a decomposition can be thought of as a piece-wise approximation of the geometry. This decomposition is fundamental to real-time physics simulation in computer graphics, where it creates a unifying representation of dynamic geometry for collision detection. A convex object also has the property of being simultaneously an explicit and implicit representation: one can interpret it explicitly as a mesh derived by computing the vertices of a convex hull, or implicitly as the collection of half-space constraints or support functions. Their implicit representation makes them particularly well suited for neural network training, as they abstract away from the topology of the geometry they need to represent. We introduce a network architecture to represent a low dimensional family of convexes. This family is automatically derived via an autoencoding process. We investigate the applications of the network including automatic convex decomposition, image to 3D reconstruction, and part-based shape retrieval.

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"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "learning to infer and execute 3d shape programs"

Connected through hyperedge HE-2 (induced by source paper "implicit geometric regularization for learning shapes"):
  with members:
    - "controlling neural level sets"
    - "sdfdiff differentiable rendering of signed distance fields for 3d shape optimization"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning elementary structures for 3d shape generation and matching"
    - "bsp net generating compact meshes via binary space partitioning"
    - "voronoinet general functional approximators with local support"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "sal sign agnostic learning of shapes from raw data"

Connected through hyperedge HE-3 (induced by source paper "nasa neural articulated shape approximation"):
  with members:
    - "implicit functions in feature space for 3d shape reconstruction and completion"
    - "amass archive of motion capture as surface shapes"
    - "deferred neural rendering image synthesis using neural textures"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "unsupervised part based disentangling of object shape and appearance"
    - "what do single view 3d reconstruction networks learn"
    - "cerberus a multi headed derenderer"
    - "bae net branched autoencoder for shape co segmentation"

Connected through hyperedge HE-4 (induced by source paper "bsp net generating compact meshes via binary space partitioning"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "logan unpaired shape transform in latent overcomplete space"
    - "bae net branched autoencoder for shape co segmentation"
    - "sdm net deep generative network for structured deformable mesh"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "im2struct recovering 3d shape structure from a single rgb image"

Connected through hyperedge HE-5 (induced by source paper "sal sign agnostic learning of shapes from raw data"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "controlling neural level sets"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning elementary structures for 3d shape generation and matching"
    - "learning implicit fields for generative shape modeling"
    - "deep sets"
    - "deep geometric prior for surface reconstruction"

Connected through hyperedge HE-6 (induced by source paper "voronoinet general functional approximators with local support"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "bsp net generating compact meshes via binary space partitioning"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "sal sign agnostic learning of shapes from raw data"
    - "point convolutional neural networks by extension operators"
    - "learning implicit fields for generative shape modeling"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"

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)