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: "a geometric approach for fully automatic chromosome segmentation"
  Abstract: A fundamental task in human chromosome analysis is chromosome segmentation. Segmentation plays an important role in chromosome karyotyping. The first step in segmentation is to remove intrusive objects such as stain debris and other noises. The next step is detection of touching and overlapping chromosomes, and the final step is separation of such chromosomes. Common methods for separation between touching chromosomes are interactive and require human intervention for correct separation between touching and overlapping chromosomes. In this paper, a geometric-based method is used for automatic detection of touching and overlapping chromosomes and separating them. The proposed scheme performs segmentation in two phases. In the first phase, chromosome clusters are detected using three geometric criteria, and in the second phase, chromosome clusters are separated using a cut-line. Most of earlier methods did not work properly in case of chromosome clusters that contained more than two chromosomes. Our method, on the other hand, is quite efficient in separation of such chromosome clusters. At each step, one separation will be performed and this algorithm is repeated until all individual chromosomes are separated. Another important point about the proposed method is that it uses the geometric features of chromosomes which are independent of the type of images and it can easily be applied to any type of images such as binary images and does not require multispectral images as well. We have applied our method to a database containing 62 touching and partially overlapping chromosomes and a success rate of 91.9% is achieved.

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"
    - "multispectral palmprint recognition using a hybrid feature"

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"
    - "multispectral palmprint recognition using a hybrid feature"

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"
    - "multispectral palmprint recognition using a hybrid feature"

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"
    - "multispectral palmprint recognition using a hybrid feature"

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)