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: "smi 2011 full paper diffusion geometric maximally stable component detection in deformable shapes"
  Abstract: Maximally stable component detection is a very popular method for feature analysis in images, mainly due to its low computation cost and high repeatability. With the recent advance of feature-based methods in geometric shape analysis, there is significant interest in finding analogous approaches in the 3D world. In this paper, we formulate a diffusion-geometric framework for stable component detection in non-rigid 3D shapes, which can be used for geometric feature detection and description. A quantitative evaluation of our method on the SHREC'10 feature detection benchmark shows its potential as a source of high-quality features.

Connected through hyperedge HE-1 (induced by source paper "self functional maps"):
  with members:
    - "steklov spectral geometry for extrinsic shape analysis"
    - "geodesic distance descriptors"
    - "on the optimality of shape and data representation in the spectral domain"
    - "spectral generalized multi dimensional scaling"
    - "discrete schemes for gaussian curvature and their convergence"

Connected through hyperedge HE-2 (induced by source paper "shapenet convolutional neural networks on non euclidean manifolds"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "learning parametric dictionaries for signals on graphs"
    - "from captions to visual concepts and back"
    - "fracking deep convolutional image descriptors"
    - "deep visual semantic alignments for generating image descriptions"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "spectral networks and locally connected networks on graphs"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-3 (induced by source paper "functional correspondence by matrix completion"):
  with members:
    - "matrix completion on graphs"
    - "matching lbo eigenspace of non rigid shapes via high order statistics"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "coupled quasi harmonic bases"
    - "multimodal diffusion geometry by joint diagonalization of laplacians"
    - "sparse modeling of intrinsic correspondences"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "sparse modeling of intrinsic correspondences"):
  with members:
    - "a correspondence less approach to matching of deformable shapes"
    - "shrec 2011 robust feature detection and description benchmark"

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)