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 benchmark for endoluminal scene segmentation of colonoscopy images"
  Abstract: Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. We provide new baselines on this dataset by training standard fully convolutional networks (FCN) for semantic segmentation and significantly outperforming, without any further post-processing, prior results in endoluminal scene segmentation.

Connected through hyperedge HE-1 (induced by source paper "a learning without forgetting approach to incorporate artifact knowledge in polyp localization tasks"):
  with members:
    - "adaptive specular reflection detection and inpainting in colonoscopy video frames"
    - "automatic colon polyp detection using region based deep cnn and post learning approaches"
    - "y net a deep convolutional neural network for polyp detection"
    - "focal loss for dense object detection"
    - "learning without forgetting"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "fully convolutional densenets for polyp segmentation in colonoscopy"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "bpds a blockchain based privacy preserving data sharing for electronic medical records"
    - "secure and trustable electronic medical records sharing using blockchain"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "psi net shape and boundary aware joint multi task deep network for medical image segmentation"):
  with members:
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "a survey on deep learning in medical image analysis"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-4 (induced by source paper "conv mcd a plug and play multi task module for medical image segmentation"):
  with members:
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-5 (induced by source paper "joint shape learning and segmentation for medical images using a minimalistic deep network"):
  with members:
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "a survey on deep learning in medical image analysis"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps"):
  with members:
    - "bts dsn deeply supervised neural network with short connections for retinal vessel segmentation"
    - "3d regression neural network for the quantification of enlarged perivascular spaces in brain mri"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "understanding autoencoders with information theoretic concepts"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "brain tumor segmentation with deep neural networks"
    - "cell detection in microscopy images with deep convolutional neural network and compressed sensing"
    - "opening the black box of deep neural networks via information"

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)