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: "top down learning for structured labeling with convolutional pseudoprior"
  Abstract: Current practice in convolutional neural networks (CNN) remains largely bottom-up and the role of top-down process in CNN for pattern analysis and visual inference is not very clear. In this paper, we propose a new method for structured labeling by developing convolutional pseudo-prior (ConvPP) on the ground-truth labels. Our method has several interesting properties: (1) compared with classical machine learning algorithms like CRFs and Structural SVM, ConvPP automatically learns rich convolutional kernels to capture both short- and long- range contexts; (2) compared with cascade classifiers like Auto-Context, ConvPP avoids the iterative steps of learning a series of discriminative classifiers and automatically learns contextual configurations; (3) compared with recent efforts combing CNN models with CRFs and RNNs, ConvPP learns convolution in the labeling space with much improved modeling capability and less manual specification; (4) compared with Bayesian models like MRFs, ConvPP capitalizes on the rich representation power of convolution by automatically learning priors built on convolutional filters. We accomplish our task using pseudo-likelihood approximation to the prior under a novel fixed-point network structure that facilitates an end-to-end learning process. We show state-of-the-art results on sequential labeling and image labeling benchmarks.

Connected through hyperedge HE-1 (induced by source paper "visual semantic information pursuit a survey"):
  with members:
    - "linknet relational embedding for scene graph"
    - "iterative visual reasoning beyond convolutions"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "large scale visual relationship understanding"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"

Connected through hyperedge HE-2 (induced by source paper "adversarial structure matching for structured prediction tasks"):
  with members:
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "taskonomy disentangling task transfer learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "regularizing deep networks by modeling and predicting label structure"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "interpreting deep visual representations via network dissection"
    - "learning affinity via spatial propagation networks"

Connected through hyperedge HE-3 (induced by source paper "disentangling propagation and generation for video prediction"):
  with members:
    - "hierarchical long term video prediction without supervision"
    - "image inpainting for irregular holes using partial convolutions"
    - "stochastic adversarial video prediction"
    - "sdcnet video prediction using spatially displaced convolution"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "stochastic video generation with a learned prior"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "dense pose transfer"

Connected through hyperedge HE-4 (induced by source paper "dense recurrent neural networks for scene labeling"):
  with members:
    - "multi context attention for human pose estimation"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "scene parsing with global context embedding"
    - "semantic object parsing with graph lstm"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "scene parsing via dense recurrent neural networks with attentional selection"):
  with members:
    - "context encoding for semantic segmentation"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "multi context attention for human pose estimation"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "scene parsing with global context embedding"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "sanet structure aware network for visual tracking"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"

Connected through hyperedge HE-6 (induced by source paper "on the iterative refinement of densely connected representation levels for semantic segmentation"):
  with members:
    - "image segmentation by iterative inference from conditional score estimation"
    - "dilated residual networks"
    - "squeeze and excitation networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "full resolution residual networks for semantic segmentation in street scenes"

Connected through hyperedge HE-7 (induced by source paper "iterative visual reasoning beyond convolutions"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "a simple neural network module for relational reasoning"
    - "scene graph generation by iterative message passing"
    - "fasttext zip compressing text classification models"
    - "spatial memory for context reasoning in object detection"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "semantic understanding of scenes through the ade20k dataset"

Connected through hyperedge HE-8 (induced by source paper "regularizing deep networks by modeling and predicting label structure"):
  with members:
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "colorization as a proxy task for visual understanding"
    - "learning representations for automatic colorization"
    - "multigrid neural architectures"
    - "ambient sound provides supervision for visual learning"
    - "unsupervised learning using sequential verification for action recognition"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)