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: "deepedge a multi scale bifurcated deep network for top down contour detection"
  Abstract: Contour detection has been a fundamental component in many image segmentation and object detection systems. Most previous work utilizes low-level features such as texture or saliency to detect contours and then use them as cues for a higher-level task such as object detection. However, we claim that recognizing objects and predicting contours are two mutually related tasks. Contrary to traditional approaches, we show that we can invert the commonly established pipeline: instead of detecting contours with low-level cues for a higher-level recognition task, we exploit object-related features as high-level cues for contour detection. #R##N#We achieve this goal by means of a multi-scale deep network that consists of five convolutional layers and a bifurcated fully-connected sub-network. The section from the input layer to the fifth convolutional layer is fixed and directly lifted from a pre-trained network optimized over a large-scale object classification task. This section of the network is applied to four different scales of the image input. These four parallel and identical streams are then attached to a bifurcated sub-network consisting of two independently-trained branches. One branch learns to predict the contour likelihood (with a classification objective) whereas the other branch is trained to learn the fraction of human labelers agreeing about the contour presence at a given point (with a regression criterion). #R##N#We show that without any feature engineering our multi-scale deep learning approach achieves state-of-the-art results in contour detection.

Connected through hyperedge HE-1 (induced by source paper "cafenet class agnostic few shot edge detection network"):
  with members:
    - "transductive few shot learning with meta learned confidence"
    - "dense classification and implanting for few shot learning"
    - "bi directional cascade network for perceptual edge detection"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "fss 1000 a 1000 class dataset for few shot segmentation"
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "dynamic feature fusion for semantic edge detection"
    - "panet few shot image semantic segmentation with prototype alignment"

Connected through hyperedge HE-2 (induced by source paper "learning temporal pose estimation from sparsely labeled videos"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "deep high resolution representation learning for human pose estimation"
    - "multi domain pose network for multi person pose estimation and tracking"
    - "pose flow efficient online pose tracking"
    - "self supervised learning of a facial attribute embedding from video"
    - "simple baselines for human pose estimation and tracking"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (induced by source paper "high level semantic feature detection a new perspective for pedestrian detection"):
  with members:
    - "dsfd dual shot face detector"
    - "cornernet detecting objects as paired keypoints"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "deep feature pyramid reconfiguration for object detection"
    - "focal loss for dense object detection"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-4 (induced by source paper "center and scale prediction a box free approach for pedestrian and face detection"):
  with members:
    - "dsfd dual shot face detector"
    - "cornernet detecting objects as paired keypoints"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "deep feature pyramid reconfiguration for object detection"
    - "deep retinex decomposition for low light enhancement"
    - "focal loss for dense object detection"

Connected through hyperedge HE-5 (induced by source paper "red net a recursive encoder decoder network for edge detection"):
  with members:
    - "casenet deep category aware semantic edge detection"
    - "richer convolutional features for edge detection"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "densely connected convolutional networks"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "deep hierarchical parsing for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "multi scale discriminative region discovery for weakly supervised object localization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self produced guidance for weakly supervised object localization"
    - "from volcano to toyshop adaptive discriminative region discovery for scene recognition"
    - "adversarial complementary learning for weakly supervised object localization"
    - "tell me where to look guided attention inference network"
    - "focal loss for dense object detection"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "receptive field block net for accurate and fast object detection"

Connected through hyperedge HE-7 (induced by source paper "faster training of mask r cnn by focusing on instance boundaries"):
  with members:
    - "auxiliary tasks in multi task learning"
    - "path aggregation network for instance segmentation"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "an overview of multi task learning in deep neural networks"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-8 (induced by source paper "edgeconnect generative image inpainting with adversarial edge learning"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "contextual based image inpainting infer match and translate"
    - "richer convolutional features for edge detection"
    - "unpaired image to image translation using cycle consistent adversarial 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)