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: "semantic segmentation of colon glands with deep convolutional neural networks and total variation segmentation"
  Abstract: Segmentation of histopathology sections is an ubiquitous requirement in digital pathology and due to the large variability of biological tissue, machine learning techniques have shown superior performance over standard image processing methods. As part of the GlaS@MICCAI2015 colon gland segmentation challenge, we present a learning-based algorithm to segment glands in tissue of benign and malignant colorectal cancer. Images are preprocessed according to the Hematoxylin-Eosin staining protocol and two deep convolutional neural networks (CNN) are trained as pixel classifiers. The CNN predictions are then regularized using a figure-ground segmentation based on weighted total variation to produce the final segmentation result. On two test sets, our approach achieves a tissue classification accuracy of 98% and 94%, making use of the inherent capability of our system to distinguish between benign and malignant tissue.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive analysis of weakly supervised semantic segmentation in different image domains"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "dense fusion classmate network for land cover classification"
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "constrained cnn losses for weakly supervised segmentation"
    - "weakly supervised semantic segmentation of satellite images"
    - "weakly supervised instance segmentation using class peak response"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "fractional wavelet scattering network and applications"):
  with members:
    - "global minimum for a finsler elastica minimal path approach"
    - "gland instance segmentation using deep multichannel neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "fully convolutional networks for semantic segmentation"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-3 (induced by source paper "a performance evaluation of convolutional neural networks for face anti spoofing"):
  with members:
    - "a performance comparison of loss functions for deep face recognition"
    - "dual cnn models for unsupervised monocular depth estimation"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "mask r cnn"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "deep representations for iris face and fingerprint spoofing detection"

Connected through hyperedge HE-4 (induced by source paper "generalizing multistain immunohistochemistry tissue segmentation using one shot color deconvolution deep neural networks"):
  with members:
    - "smoothgrad removing noise by adding noise"
    - "detecting cancer metastases on gigapixel pathology images"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "visualizing deep convolutional neural networks using natural pre images"
    - "understanding neural networks through deep visualization"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "gland instance segmentation using deep multichannel neural networks"):
  with members:
    - "instance sensitive fully convolutional networks"
    - "object detection free instance segmentation with labeling transformations"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "gland instance segmentation by deep multichannel side supervision"
    - "learning to refine object segments"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "learning to segment object candidates"

Connected through hyperedge HE-6 (induced by source paper "gland segmentation in colon histology images the glas challenge contest"):
  with members:
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "fully convolutional networks for semantic segmentation"

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)