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: "angiodysplasia detection and localization using deep convolutional neural networks"
  Abstract: Accurate detection and localization for angiodysplasia lesions is an important problem in early stage diagnostics of gastrointestinal bleeding and anemia. Gold standard for angiodysplasia detection and localization is performed using wireless capsule endoscopy. This pill-like device is able to produce thousand of high enough resolution images during one passage through gastrointestinal tract. In this paper we present our solution for MICCAI 2017 Endoscopic Vision SubChallenge: Angiodysplasia Detection and Localization its further improvements over the state-of-the-art results using several deep neural network architectures. It addresses the binary segmentation problem, where every pixel in an image is labeled as an angiodysplasia lesions or background. Then, we analyze connected components of each predicted mask. Based on the analysis we developed a classifier that predict angiodysplasia lesions (binary variable) and a detector for their localization (center of a component). In this setting, our approach demonstrates one of the top results in every task subcategory for angiodysplasia detection and localization thereby providing state-of-the-art performance for these problems. The source code for our solution is made publicly available at https://github.com/ternaus/angiodysplasia-segmentation.

Connected through hyperedge HE-1 (induced by source paper "albumentations fast and flexible image augmentations"):
  with members:
    - "camera model identification using convolutional neural networks"
    - "paediatric bone age assessment using deep convolutional neural networks"
    - "fully convolutional network for automatic road extraction from satellite imagery"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "autoaugment learning augmentation policies from data"
    - "feature pyramid network for multi class land segmentation"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-2 (induced by source paper "deepflash turning a flash selfie into a studio portrait"):
  with members:
    - "single image reflection separation with perceptual losses"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "learning to see in the dark"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "deep bilateral learning for real time image enhancement"
    - "linknet exploiting encoder representations for efficient semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "fixed point convolutional neural network for real time video processing in fpga"):
  with members:
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "hydrocephalus verification on brain magnetic resonance images with deep convolutional neural networks and transfer learning technique"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "using j k fold cross validation to reduce variance when tuning nlp models"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "retina u net embarrassingly simple exploitation of segmentation supervision for medical object detection"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "what can help pedestrian detection"
    - "megdet a large mini batch object detector"
    - "single shot object detection with enriched semantics"

Connected through hyperedge HE-6 (induced by source paper "automatic instrument segmentation in robot assisted surgery using deep learning"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "fixed point convolutional neural network for real time video processing in fpga"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "paediatric bone age assessment using deep convolutional neural networks"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
    - "linknet exploiting encoder representations for efficient semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "feature pyramid network for multi class land segmentation"):
  with members:
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "focal loss for dense object detection"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "satellite imagery feature detection using deep convolutional neural network a kaggle competition"
    - "mask r cnn"

Connected through hyperedge HE-8 (induced by source paper "camera model identification using convolutional neural networks"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "paediatric bone age assessment using deep convolutional neural networks"
    - "fully convolutional network for automatic road extraction from satellite imagery"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "feature pyramid network for multi class land segmentation"
    - "aligned and non aligned double jpeg detection using convolutional neural networks"

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)