human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"):
  Abstract: This article describes the design, implementation, and results of the latest installment of the dermoscopic image analysis benchmark challenge. The goal is to support research and development of algorithms for automated diagnosis of melanoma, the most lethal skin cancer. The challenge was divided into 3 tasks: lesion segmentation, feature detection, and disease classification. Participation involved 593 registrations, 81 pre-submissions, 46 finalized submissions (including a 4-page manuscript), and approximately 50 attendees, making this the largest standardized and comparative study in this field to date. While the official challenge duration and ranking of participants has concluded, the dataset snapshots remain available for further research and development.

Includes members:
  Member "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bi directional dermoscopic feature learning and multi scale consistent decision fusion for skin lesion segmentation")
  Member "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bi directional dermoscopic feature learning and multi scale consistent decision fusion for skin lesion segmentation")
  Member "incorporating the knowledge of dermatologists to convolutional neural networks for the diagnosis of skin lesions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "solo or ensemble choosing a cnn architecture for melanoma classification")
  Member "skin lesion analysis towards melanoma detection using deep learning network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving disentangled representation learning with the beta bernoulli process")
  Member "recod titans at isic challenge 2017":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "solo or ensemble choosing a cnn architecture for melanoma classification")
  Member "deep learning ensembles for melanoma recognition in dermoscopy images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "assessing robustness of deep learning methods in dermatological workflow")
  Member "an overview of melanoma detection in dermoscopy images using image processing and machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "visual diagnosis of dermatological disorders human and machine performance")
  Member "lesion border detection in dermoscopy images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks")

Question: Please tell me which class the source paper 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)