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: "learning deep structured multi scale features using attention gated crfs for contour prediction"
  Abstract: Recent works have shown that exploiting multi-scale representations deeply learned via convolutional neural networks (CNN) is of tremendous importance for accurate contour detection. This paper presents a novel approach for predicting contours which advances the state of the art in two fundamental aspects, i.e. multi-scale feature generation and fusion. Different from previous works directly consider- ing multi-scale feature maps obtained from the inner layers of a primary CNN architecture, we introduce a hierarchical deep model which produces more rich and complementary representations. Furthermore, to refine and robustly fuse the representations learned at different scales, the novel Attention-Gated Conditional Random Fields (AG-CRFs) are proposed. The experiments ran on two publicly available datasets (BSDS500 and NYUDv2) demonstrate the effectiveness of the latent AG-CRF model and of the overall hierarchical framework.

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning of camera pose with compositional re estimation"):
  with members:
    - "on the convergence of adam and beyond"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "megadepth learning single view depth prediction from internet photos"

Connected through hyperedge HE-2 (induced by source paper "dynamic graph message passing networks"):
  with members:
    - "dynamic graph convolutional networks"
    - "pay less attention with lightweight and dynamic convolutions"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-3 (induced by source paper "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"):
  with members:
    - "learning to exploit the prior network knowledge for weakly supervised semantic segmentation"
    - "hybrid task cascade for instance segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "weakly and semi supervised panoptic segmentation"

Connected through hyperedge HE-4 (induced by source paper "bi directional cascade network for perceptual edge detection"):
  with members:
    - "learning to predict crisp boundaries"
    - "richer convolutional features for edge detection"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "multi scale dense networks for resource efficient image classification"
    - "srn side output residual network for object symmetry detection in the wild"
    - "hierarchical multi scale attention networks for action recognition"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "convolutional oriented boundaries"

Connected through hyperedge HE-5 (induced by source paper "crowd counting with deep structured scale integration network"):
  with members:
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "context aware crowd counting"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd counting"
    - "crowd counting by adaptively fusing predictions from an image pyramid"
    - "attentive crowd flow machines"

Connected through hyperedge HE-6 (induced by source paper "structured modeling of joint deep feature and prediction refinement for salient object detection"):
  with members:
    - "bayesian loss for crowd count estimation with point supervision"
    - "saliency integration an arbitrator model"
    - "deeply supervised salient object detection with short connections"
    - "salient objects in clutter bringing salient object detection to the foreground"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "reverse attention for salient object detection"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "picanet learning pixel wise contextual attention for saliency detection"

Connected through hyperedge HE-7 (induced by source paper "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"):
  with members:
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "recurrent scene parsing with perspective understanding in the loop"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-8 (induced by source paper "monocular depth estimation using multi scale continuous crfs as sequential deep networks"):
  with members:
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "unsupervised learning of depth and ego motion from video"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "semi supervised deep learning for monocular depth map prediction"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "unsupervised monocular depth estimation with left right consistency"
    - "a unified multi scale deep convolutional neural network for fast object detection"

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)