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: "beyond pixels a comprehensive survey from bottom up to semantic image segmentation and cosegmentation"
  Abstract: Image segmentation refers to the process to divide an image into nonoverlapping meaningful regions according to human perception, which has become a classic topic since the early ages of computer vision. A lot of research has been conducted and has resulted in many applications. However, while many segmentation algorithms exist, yet there are only a few sparse and outdated summarizations available, an overview of the recent achievements and issues is lacking. We aim to provide a comprehensive review of the recent progress in this field. Covering 180 publications, we give an overview of broad areas of segmentation topics including not only the classic bottom-up approaches, but also the recent development in superpixel, interactive methods, object proposals, semantic image parsing and image cosegmentation. In addition, we also review the existing influential datasets and evaluation metrics. Finally, we suggest some design flavors and research directions for future research in image segmentation.

Connected through hyperedge HE-1 (induced by source paper "waterfall atrous spatial pooling architecture for efficient semantic segmentation"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "deformable convolutional networks"
    - "squeeze and excitation networks"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a review on deep learning techniques applied to semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep multiphase level set for scene parsing"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning deep representations for semantic image parsing a comprehensive overview"
    - "ocnet object context network for scene parsing"
    - "unified perceptual parsing for scene understanding"
    - "moe spnet a mixture of experts scene parsing network"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "context encoding for semantic segmentation"
    - "loss max pooling for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "pointwise attention based atrous convolutional neural networks"):
  with members:
    - "point attention network for semantic segmentation of 3d point clouds"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "deformable filter convolution for point cloud reasoning"
    - "kpconv flexible and deformable convolution for point clouds"
    - "dilated point convolutions on the receptive field of point convolutions"
    - "randla net efficient semantic segmentation of large scale point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "tangent convolutions for dense prediction in 3d"

Connected through hyperedge HE-4 (induced by source paper "deep object co segmentation via spatial semantic network modulation"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "saliency for fine grained object recognition in domains with scarce training data"
    - "show match and segment joint learning of semantic matching and object co segmentation"
    - "semantic aware attention based deep object co segmentation"
    - "cross modulation networks for few shot learning"
    - "efficient video object segmentation via network modulation"
    - "deep object co segmentation"

Connected through hyperedge HE-5 (induced by source paper "operational neural networks"):
  with members:
    - "heterogeneous multilayer generalized operational perceptron"
    - "progressive operational perceptron with memory"
    - "efficient neural architecture search via parameter sharing"
    - "layer wise training of deep networks using kernel similarity"
    - "learning transferable architectures for scalable image recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "semantic segmentation using adversarial networks"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "ev segnet semantic segmentation for event based cameras"):
  with members:
    - "realtime time synchronized event based stereo"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "ev flownet self supervised optical flow estimation for event based cameras"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "cnn based segmentation of medical imaging data"

Connected through hyperedge HE-7 (induced by source paper "accelerated inference in markov random fields via smooth riemannian optimization"):
  with members:
    - "bonnet an open source training and deployment framework for semantic segmentation in robotics using cnns"
    - "large scale binary quadratic optimization using semidefinite relaxation and applications"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "a survey of semantic segmentation"
    - "efficient sdp inference for fully connected crfs based on low rank decomposition"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"

Connected through hyperedge HE-8 (induced by source paper "subsurface structure analysis using computational interpretation and learning a visual signal processing perspective"):
  with members:
    - "salsi a new seismic attribute for salt dome detection"
    - "the role of visual saliency in the automation of seismic interpretation"
    - "lstm a search space odyssey"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "nips 2016 tutorial generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pixelnet towards a general pixel level architecture"

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)