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: "feature level fusion of face and fingerprint biometrics"
  Abstract: The aim of this paper is to study the fusion at feature extraction level for face and fingerprint biometrics. The proposed approach is based on the fusion of the two traits by extracting independent feature pointsets from the two modalities, and making the two pointsets compatible for concatenation. Moreover, to handle the problem of curse of dimensionality, the feature pointsets are properly reduced in dimension. Different feature reduction techniques are implemented, prior and after the feature pointsets fusion, and the results are duly recorded. The fused feature pointset for the database and the query face and fingerprint images are matched using techniques based on either the point pattern matching, or the Delaunay triangulation. Comparative experiments are conducted on chimeric and real databases, to assess the actual advantage of the fusion performed at the feature extraction level, in comparison to the matching score level.

Connected through hyperedge HE-1 (induced by source paper "expressing facial structure and appearance information in frequency domain for face recognition"):
  with members:
    - "feature selection via simultaneous sparse approximation for person specific face verification"

Connected through hyperedge HE-2 (induced by source paper "quality based multimodal classification using tree structured sparsity"):
  with members:
    - "a brief summary of dictionary learning based approach for classification revised"
    - "learning task grouping and overlap in multi task learning"

Connected through hyperedge HE-3 (induced by source paper "enhancing the accuracy of biometric feature extraction fusion using gabor filter and mahalanobis distance algorithm"):
  with members:
    - "face recognition based on svm and 2dpca"

Connected through hyperedge HE-4 (induced by source paper "feature level fusion of face and palmprint biometrics by isomorphic graph based improved k medoids partitioning"):
  with members:
    - "face identification by sift based complete graph topology"
    - "face recognition by fusion of local and global matching scores using ds theory an evaluation with uni classifier and multi classifier paradigm"

Connected through hyperedge HE-5 (induced by source paper "sift based ear recognition by fusion of detected keypoints from color similarity slice regions"):
  with members:
    - "face recognition by fusion of local and global matching scores using ds theory an evaluation with uni classifier and multi classifier paradigm"

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)