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: "salient local 3d features for 3d shape retrieval"
  Abstract: In this paper we describe a new formulation for the 3D salient local features based on the voxel grid inspired by the Scale#R##N#Invariant Feature Transform (SIFT). We use it to identify the salient keypoints (invariant points) on a 3D voxelized#R##N#model and calculate invariant 3D local feature descriptors at these keypoints. We then use the bag of words approach on#R##N#the 3D local features to represent the 3D models for shape retrieval. The advantages of the method are that it can be#R##N#applied to rigid as well as to articulated and deformable 3D models. Finally, this approach is applied for 3D Shape#R##N#Retrieval on the McGill articulated shape benchmark and then the retrieval results are presented and compared to other#R##N#methods.

Connected through hyperedge HE-1 (induced by source paper "3d keypoint detection based on deep neural network with sparse autoencoder"):
  with members:
    - "3d interest point detection via discriminative learning"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-2 (induced by source paper "mesh interest point detection based on geometric measures and sparse refinement"):
  with members:
    - "3d interest point detection via discriminative learning"
    - "iterative hard thresholding for compressed sensing"

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)