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: "unsupervised learning of edges"
  Abstract: Data-driven approaches for edge detection have proven effective and achieve top results on modern benchmarks. However, all current data-driven edge detectors require manual supervision for training in the form of hand-labeled region segments or object boundaries. Specifically, human annotators mark semantically meaningful edges which are subsequently used for training. Is this form of strong, high-level supervision actually necessary to learn to accurately detect edges? In this work we present a simple yet effective approach for training edge detectors without human supervision. To this end we utilize motion, and more specifically, the only input to our method is noisy semi-dense matches between frames. We begin with only a rudimentary knowledge of edges (in the form of image gradients), and alternate between improving motion estimation and edge detection in turn. Using a large corpus of video data, we show that edge detectors trained using our unsupervised scheme approach the performance of the same methods trained with full supervision (within 3-5%). Finally, we show that when using a deep network for the edge detector, our approach provides a novel pre-training scheme for object detection.

Connected through hyperedge HE-1 (induced by source paper "implicit label augmentation on partially annotated clips via temporally adaptive features learning"):
  with members:
    - "panoptic segmentation"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"

Connected through hyperedge HE-2 (induced by source paper "data efficient image recognition with contrastive predictive coding"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "fast autoaugment"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "unsupervised data augmentation for consistency training"

Connected through hyperedge HE-3 (induced by source paper "learning correspondence from the cycle consistency of time"):
  with members:
    - "recycle gan unsupervised video retargeting"
    - "deep clustering for unsupervised learning of visual features"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "efficient video object segmentation via network modulation"
    - "adaptive temporal encoding network for video instance level human parsing"
    - "switchable temporal propagation network"
    - "tracking emerges by colorizing videos"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "deep crisp boundaries from boundaries to higher level tasks"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "understanding convolution for semantic segmentation"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "object contour detection with a fully convolutional encoder decoder network"

Connected through hyperedge HE-6 (induced by source paper "topology maintained structure encoding"):
  with members:
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "universal sentence encoder"
    - "progressive growing of gans for improved quality stability and variation"
    - "richer convolutional features for edge detection"
    - "improved training of wasserstein gans"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "deep ten texture encoding network"
    - "vote3deep fast object detection in 3d point clouds using efficient convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning visual features under motion invariance"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "cognitive action laws the case of visual features"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "acquisition of visual features through probabilistic spike timing dependent plasticity"
    - "learning features by watching objects move"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised learning of visual representations using videos"
    - "unsupervised learning of spatiotemporally coherent metrics"

Connected through hyperedge HE-8 (induced by source paper "self supervised spatiotemporal feature learning via video rotation prediction"):
  with members:
    - "self supervised visual feature learning with deep neural networks a survey"
    - "real world anomaly detection in surveillance videos"
    - "moments in time dataset one million videos for event understanding"
    - "videocapsulenet a simplified network for action detection"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "cross and learn cross modal self supervision"
    - "unsupervised representation learning by predicting image rotations"

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)