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: "deformation driven shape correspondence via shape recognition"
  Abstract: Many approaches to shape comparison and recognition start by establishing a shape correspondence. We "turn the table" and show that quality shape correspondences can be obtained by performing many shape recognition tasks. What is more, the method we develop computes a fine-grained, topology-varying part correspondence between two 3D shapes where the core evaluation mechanism only recognizes shapes globally. This is made possible by casting the part correspondence problem in a deformation-driven framework and relying on a data-driven "deformation energy" which rates visual similarity between deformed shapes and models from a shape repository. Our basic premise is that if a correspondence between two chairs (or airplanes, bicycles, etc.) is correct, then a reasonable deformation between the two chairs anchored on the correspondence ought to produce plausible, "chair-like" in-between shapes.   Given two 3D shapes belonging to the same category, we perform a top-down, hierarchical search for part correspondences. For a candidate correspondence at each level of the search hierarchy, we deform one input shape into the other, while respecting the correspondence, and rate the correspondence based on how well the resulting deformed shapes resemble other shapes from ShapeNet belonging to the same category as the inputs. The resemblance, i.e., plausibility, is measured by comparing multi-view depth images over category-specific features learned for the various shape categories. We demonstrate clear improvements over state-of-the-art approaches through tests covering extensive sets of man-made models with rich geometric and topological variations.

Connected through hyperedge HE-1 (induced by source paper "pq net a generative part seq2seq network for 3d shapes"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning part generation and assembly for structure aware shape synthesis"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "sdm net deep generative network for structured deformable mesh"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"

Connected through hyperedge HE-2 (induced by source paper "learning part generation and assembly for structure aware shape synthesis"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "structure aware shape synthesis"
    - "learning fuzzy set representations of partial shapes on dual embedding spaces"
    - "learning implicit fields for generative shape modeling"
    - "learning to generate the unseen via part synthesis and composition"

Connected through hyperedge HE-3 (induced by source paper "learning to group and label fine grained shape components"):
  with members:
    - "learning hierarchical shape segmentation and labeling from online repositories"
    - "complementme weakly supervised component suggestions for 3d modeling"
    - "3d shape segmentation with projective convolutional networks"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "shapenet an information rich 3d model repository"
    - "conditional random fields as recurrent neural networks"
    - "fast r cnn"

Connected through hyperedge HE-4 (induced by source paper "im2struct recovering 3d shape structure from a single rgb image"):
  with members:
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "learning shape abstractions by assembling volumetric primitives"
    - "3d shape segmentation with projective convolutional networks"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "a two streamed network for estimating fine scaled depth maps from single rgb images"
    - "learning a predictable and generative vector representation for objects"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "deeper depth prediction with fully convolutional residual networks"

Connected through hyperedge HE-5 (induced by source paper "scores shape composition with recursive substructure priors"):
  with members:
    - "complementme weakly supervised component suggestions for 3d modeling"
    - "learning a predictable and generative vector representation for objects"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "3d shapenets a deep representation for volumetric shapes"

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.GR(Graphics)