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: "scene parsing with global context embedding"
  Abstract: We present a scene parsing method that utilizes global context information based on both the parametric and non- parametric models. Compared to previous methods that only exploit the local relationship between objects, we train a context network based on scene similarities to generate feature representations for global contexts. In addition, these learned features are utilized to generate global and spatial priors for explicit classes inference. We then design modules to embed the feature representations and the priors into the segmentation network as additional global context cues. We show that the proposed method can eliminate false positives that are not compatible with the global context representations. Experiments on both the MIT ADE20K and PASCAL Context datasets show that the proposed method performs favorably against existing methods.

Connected through hyperedge HE-1 (induced by source paper "semantic correlation promoted shape variant context for segmentation"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "aanet attribute attention network for person re identifications"
    - "scene graph generation with external knowledge and image reconstruction"
    - "towards robust curve text detection with conditional spatial expansion"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "a network structure to explicitly reduce confusion errors in semantic segmentation"
    - "a classification refinement strategy for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "adaptive context network for scene parsing"):
  with members:
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "semantic correlation promoted shape variant context for segmentation"
    - "boundary aware feature propagation for scene segmentation"
    - "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"
    - "unified perceptual parsing for scene understanding"

Connected through hyperedge HE-3 (induced by source paper "multi scale dual branch fully convolutional network for hand parsing"):
  with members:
    - "bag of tricks for image classification with convolutional neural networks"
    - "progressive refinement a method of coarse to fine image parsing using stacked network"
    - "multi scale spatially asymmetric recalibration for image classification"
    - "crossnet an end to end reference based super resolution network using cross scale warping"
    - "video object segmentation with joint re identification and attention aware mask propagation"
    - "holistic instance level human parsing"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "learning to estimate 3d hand pose from single rgb images"

Connected through hyperedge HE-4 (induced by source paper "deep context aware kernel networks"):
  with members:
    - "multi modal multi scale deep learning for large scale image annotation"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "deepcontext context encoding neural pathways for 3d holistic scene understanding"
    - "sift meets cnn a decade survey of instance retrieval"
    - "deep residual learning for image recognition"
    - "training very deep networks"
    - "going deeper with convolutions"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-5 (induced by source paper "efficient coarse to fine non local module for the detection of small objects"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "ocnet object context network for scene parsing"
    - "yolov3 an incremental improvement"
    - "compact generalized non local network"
    - "auto context r cnn"
    - "relational inductive biases deep learning and graph networks"
    - "non local recurrent network for image restoration"
    - "couplenet coupling global structure with local parts for object detection"

Connected through hyperedge HE-6 (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"
    - "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"
    - "pixel recurrent neural networks"

Connected through hyperedge HE-7 (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"
    - "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"
    - "video scene parsing with predictive feature learning"

Connected through hyperedge HE-8 (induced by source paper "learning to adapt structured output space for semantic segmentation"):
  with members:
    - "synscapes a photorealistic synthetic dataset for street scene parsing"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep image harmonization"
    - "unsupervised domain adaptation for face recognition in unlabeled videos"

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)