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: "image parsing with a wide range of classes and scene level context"
  Abstract: This paper presents a nonparametric scene parsing approach that improves the overall accuracy, as well as the coverage of foreground classes in scene images. We first improve the label likelihood estimates at superpixels by merging likelihood scores from different probabilistic classifiers. This boosts the classification performance and enriches the representation of less-represented classes. Our second contribution consists of incorporating semantic context in the parsing process through global label costs. Our method does not rely on image retrieval sets but rather assigns a global likelihood estimate to each label, which is plugged into the overall energy function. We evaluate our system on two large-scale datasets, SIFTflow and LMSun. We achieve state-of-the-art performance on the SIFTflow dataset and near-record results on LMSun.

Connected through hyperedge HE-1 (induced by source paper "multi hypothesis contextual modeling for semantic segmentation"):
  with members:
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "conditional random fields as recurrent neural networks"
    - "parsenet looking wider to see better"
    - "multi scale context aggregation by dilated convolutions"
    - "adaptive nonparametric image parsing"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-2 (induced by source paper "deep learning for imbalance data classification using class expert generative adversarial network"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "cost sensitive learning of deep feature representations from imbalanced data"
    - "adam a method for stochastic optimization"
    - "feedforward semantic segmentation with zoom out features"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-3 (induced by source paper "learning deep representations for scene labeling with semantic context guided supervision"):
  with members:
    - "learnable histogram statistical context features for deep neural networks"
    - "region based semantic segmentation with end to end training"
    - "semantic image segmentation via deep parsing network"
    - "deep hierarchical parsing for semantic segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning visual features from large weakly supervised data"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "region based semantic segmentation with end to end training"):
  with members:
    - "deep hierarchical parsing for semantic segmentation"
    - "joint calibration for semantic segmentation"
    - "conditional random fields as recurrent neural networks"
    - "fast r cnn"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "convolutional feature masking for joint object and stuff segmentation"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "scene parsing with integration of parametric and non parametric models"):
  with members:
    - "dense recurrent neural networks for scene labeling"
    - "conditional random fields as recurrent neural networks"
    - "video tracking using learned hierarchical features"
    - "exemplar based deep discriminative and shareable feature learning for scene image classification"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "scene labeling through knowledge based rules employing constrained integer linear programing"):
  with members:
    - "sparse modeling for image and vision processing"
    - "scene parsing with multiscale feature learning purity trees and optimal covers"

Connected through hyperedge HE-7 (induced by source paper "joint calibration for semantic segmentation"):
  with members:
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "deep hierarchical parsing for semantic segmentation"
    - "conditional random fields as recurrent neural networks"
    - "fully connected deep structured networks"
    - "efficient piecewise training of deep structured models for semantic segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "simultaneous detection and segmentation"

Connected through hyperedge HE-8 (induced by source paper "sample and filter nonparametric scene parsing via efficient filtering"):
  with members:
    - "deep hierarchical parsing for semantic segmentation"
    - "fully convolutional networks for semantic segmentation"
    - "scene parsing with multiscale feature learning purity trees and optimal covers"

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)