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: "pixelnet representation of the pixels by the pixels and for the pixels"
  Abstract: We explore design principles for general pixel-level prediction problems, from low-level edge detection to mid-level surface normal estimation to high-level semantic segmentation. Convolutional predictors, such as the fully-convolutional network (FCN), have achieved remarkable success by exploiting the spatial redundancy of neighboring pixels through convolutional processing. Though computationally efficient, we point out that such approaches are not statistically efficient during learning precisely because spatial redundancy limits the information learned from neighboring pixels. We demonstrate that stratified sampling of pixels allows one to (1) add diversity during batch updates, speeding up learning; (2) explore complex nonlinear predictors, improving accuracy; and (3) efficiently train state-of-the-art models tabula rasa (i.e., "from scratch") for diverse pixel-labeling tasks. Our single architecture produces state-of-the-art results for semantic segmentation on PASCAL-Context dataset, surface normal estimation on NYUDv2 depth dataset, and edge detection on BSDS.

Connected through hyperedge HE-1 (induced by source paper "mti net multi scale task interaction networks for multi task learning"):
  with members:
    - "which tasks should be learned together in multi task learning"
    - "branched multi task networks deciding what layers to share"
    - "deep high resolution representation learning for visual recognition"
    - "pattern affinitive propagation across depth surface normal and semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "panoptic feature pyramid networks"
    - "attentive single tasking of multiple tasks"
    - "a modulation module for multi task learning with applications in image retrieval"

Connected through hyperedge HE-2 (induced by source paper "attentive single tasking of multiple tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "taskonomy disentangling task transfer learning"
    - "unified perceptual parsing for scene understanding"
    - "efficient video object segmentation via network modulation"

Connected through hyperedge HE-3 (induced by source paper "pifu pixel aligned implicit function for high resolution clothed human digitization"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "learning category specific mesh reconstruction from image collections"
    - "group normalization"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "video based reconstruction of 3d people models"

Connected through hyperedge HE-4 (induced by source paper "3d packing for self supervised monocular depth estimation"):
  with members:
    - "two stream networks for self supervised ego motion estimation"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "revisiting self supervised visual representation learning"
    - "group normalization"
    - "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"
    - "hartley spectral pooling for deep learning"

Connected through hyperedge HE-5 (induced by source paper "hierarchical autoregressive image models with auxiliary decoders"):
  with members:
    - "preventing posterior collapse with delta vaes"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "deep contextualized word representations"
    - "large scale gan training for high fidelity natural image synthesis"
    - "autoregressive quantile networks for generative modeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a style based generator architecture for generative adversarial networks"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"

Connected through hyperedge HE-6 (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"
    - "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"
    - "learning to refine object segments"

Connected through hyperedge HE-7 (induced by source paper "sparse2dense from direct sparse odometry to dense 3d reconstruction"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "single view stereo matching"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "dilated residual networks"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "undeepvo monocular visual odometry through unsupervised deep learning"

Connected through hyperedge HE-8 (induced by source paper "high throughput quantitative metallography for complex microstructures using deep learning a case study in ultrahigh carbon steel"):
  with members:
    - "advanced steel microstructural classification by deep learning methods"
    - "decoupled weight decay regularization"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

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)