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 "semi supervised skin lesion segmentation via transformation consistent self ensembling model"):
  Abstract: Automatic skin lesion segmentation on dermoscopic images is an essential component in computer-aided diagnosis of melanoma. Recently, many fully supervised deep learning based methods have been proposed for automatic skin lesion segmentation. However, these approaches require massive pixel-wise annotation from experienced dermatologists, which is very costly and time-consuming. In this paper, we present a novel semi-supervised method for skin lesion segmentation by leveraging both labeled and unlabeled data. The network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data. In this paper, we present a novel semi-supervised method for skin lesion segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data. Our method encourages a consistent prediction for unlabeled images using the outputs of the network-in-training under different regularizations, so that it can utilize the unlabeled data. To utilize the unlabeled data, our method encourages the consistent predictions of the network-in-training for the same input under different regularizations. Aiming for the semi-supervised segmentation problem, we enhance the effect of regularization for pixel-level predictions by introducing a transformation, including rotation and flipping, consistent scheme in our self-ensembling model. With only 300 labeled training samples, our method sets a new record on the benchmark of the International Skin Imaging Collaboration (ISIC) 2017 skin lesion segmentation challenge. Such a result clearly surpasses fully-supervised state-of-the-arts that are trained with 2000 labeled data.

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 "adversarial learning for semi supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised domain adaptation for mobile semantic segmentation based on cycle consistency and feature alignment")
  Member "deeply supervised rotation equivariant network for lesion segmentation in dermoscopy images":
    also connected through other hyperedges:
      (none)
  Member "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bi directional dermoscopic feature learning and multi scale consistent decision fusion for skin lesion segmentation")
  Member "automatic skin lesion segmentation using semi supervised learning technique":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self learning ai framework for skin lesion image segmentation and classification")
  Member "isic 2017 skin lesion analysis towards melanoma detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation consistent self ensembling model for semi supervised medical image segmentation")
  Member "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "solo or ensemble choosing a cnn architecture for melanoma classification")
  Member "harmonic networks deep translation and rotation equivariance":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on learning sets of symmetric elements")

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)