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: "an improved objective evaluation measure for border detection in dermoscopy images"
  Abstract: Background: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human i nterpretation, dermoscopy image analysis has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders. Although numerous methods have been developed for the detection of lesion borders, very few studies were comprehensive in the evaluation of their results. Methods: In this paper, we evaluate five recent border detection methods on a set of 90 dermoscopy images using three sets of dermatologist-drawn borders as the ground-truth. In contrast to previous work, we utilize an objective measure, the Normalized Probabilistic Rand Index, which takes into account the variations in the ground-truth images. Conclusion: The results demonstrate that the differences between four of the evaluated border detection methods are in fact smaller than those predicted by the commonly used XOR measure.

Connected through hyperedge HE-1 (induced by source paper "a survey of semantic segmentation"):
  with members:
    - "instance aware semantic segmentation via multi task network cascades"
    - "conditional random fields as recurrent neural networks"
    - "pixel wise segmentation of street with neural networks"
    - "fully convolutional networks for semantic segmentation"
    - "recurrent convolutional neural networks for scene parsing"

Connected through hyperedge HE-2 (induced by source paper "an overview of melanoma detection in dermoscopy images using image processing and machine learning"):
  with members:
    - "lesion border detection in dermoscopy images using ensembles of thresholding methods"
    - "lesion border detection in dermoscopy images"
    - "automatic detection of blue white veil and related structures in dermoscopy images"

Connected through hyperedge HE-3 (induced by source paper "lesion border detection in dermoscopy images"):
  with members:
    - "nonlinear vector filtering for impulsive noise removal from color images"

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)