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: "shrec 2011 robust feature detection and description benchmark"
  Abstract: Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. SHREC'11 robust feature detection and description benchmark simulates the feature detection and description stages of feature-based shape retrieval algorithms. The benchmark tests the performance of shape feature detectors and descriptors under a wide variety of transformations. The benchmark allows evaluating how algorithms cope with certain classes of transformations and strength of the transformations that can be dealt with. The present paper is a report of the SHREC'11 robust feature detection and description benchmark results.

Connected through hyperedge HE-1 (induced by source paper "a performance evaluation of correspondence grouping methods for 3d rigid data matching"):
  with members:
    - "performance evaluation of 3d correspondence grouping algorithms"
    - "guaranteed outlier removal for point cloud registration with correspondences"
    - "matchbench an evaluation of feature matchers"
    - "in search of inliers 3d correspondence by local and global voting"
    - "rotational projection statistics for 3d local surface description and object recognition"

Connected through hyperedge HE-2 (induced by source paper "performance evaluation of 3d correspondence grouping algorithms"):
  with members:
    - "in search of inliers 3d correspondence by local and global voting"
    - "rotational projection statistics for 3d local surface description and object recognition"

Connected through hyperedge HE-3 (induced by source paper "histograms of gaussian normal distribution for feature matching in clutter scenes"):
  with members:
    - "rotational projection statistics for 3d local surface description and object recognition"

Connected through hyperedge HE-4 (induced by source paper "detection and characterization of intrinsic symmetry"):
  with members:
    - "sparse modeling of intrinsic correspondences"

Connected through hyperedge HE-5 (induced by source paper "sparse modeling of intrinsic correspondences"):
  with members:
    - "smi 2011 full paper diffusion geometric maximally stable component detection in deformable shapes"
    - "a correspondence less approach to matching of deformable 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.CV(Computer Vision and Pattern Recognition)