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: "multispectral palmprint recognition using a hybrid feature"
  Abstract: Personal identification problem has been a major field of research in recent years. Biometrics-based technologies that exploit fingerprints, iris, face, voice and palmprints, have been in the center of attention to solve this problem. Palmprints can be used instead of fingerprints that have been of the earliest of these biometrics technologies. A palm is covered with the same skin as the fingertips but has a larger surface, giving us more information than the fingertips. The major features of the palm are palm-lines, including principal lines, wrinkles and ridges. Using these lines is one of the most popular approaches towards solving the palmprint recognition problem. Another robust feature is the wavelet energy of palms. In this paper we used a hybrid feature which combines both of these features. %Moreover, multispectral analysis is applied to improve the performance of the system. At the end, minimum distance classifier is used to match test images with one of the training samples. The proposed algorithm has been tested on a well-known multispectral palmprint dataset and achieved an average accuracy of 98.8\%.

Connected through hyperedge HE-1 (induced by source paper "screen content image segmentation using least absolute deviation fitting"):
  with members:
    - "multispectral palmprint recognition using textural features"
    - "a robust regression approach for background foreground segmentation"
    - "a geometric approach for fully automatic chromosome segmentation"

Connected through hyperedge HE-2 (induced by source paper "on the power of joint wavelet dct features for multispectral palmprint recognition"):
  with members:
    - "iris recognition using scattering transform and textural features"
    - "multi task image classification via collaborative hierarchical spike and slab priors"
    - "multispectral palmprint recognition using textural features"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"
    - "a geometric approach for fully automatic chromosome segmentation"

Connected through hyperedge HE-3 (induced by source paper "highly accurate multispectral palmprint recognition using statistical and wavelet features"):
  with members:
    - "iris recognition using scattering transform and textural features"
    - "multispectral palmprint recognition using textural features"
    - "a geometric approach for fully automatic chromosome segmentation"

Connected through hyperedge HE-4 (induced by source paper "multispectral palmprint recognition using textural features"):
  with members:
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"
    - "a geometric approach for fully automatic chromosome segmentation"

Connected through hyperedge HE-5 (induced by source paper "a robust regression approach for background foreground segmentation"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling"

Connected through hyperedge HE-6 (induced by source paper "multispectral palmprint encoding and recognition"):
  with members:
    - "making palm print matching mobile"

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)