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 segmentation using sparse subset selection"
  Abstract: In this paper, we present a new image segmentation method based on the concept of sparse subset selection. Starting with an over-segmentation, we adopt local spectral histogram features to encode the visual information of the small segments into high-dimensional vectors, called superpixel features. Then, the superpixel features are fed into a novel convex model which efficiently leverages the features to group the superpixels into a proper number of coherent regions. Our model automatically determines the optimal number of coherent regions and superpixels assignment to shape final segments. To solve our model, we propose a numerical algorithm based on the alternating direction method of multipliers (ADMM), whose iterations consist of two highly parallelizable sub-problems. We show each sub-problem enjoys closed-form solution which makes the ADMM iterations computationally very efficient. Extensive experiments on benchmark image segmentation datasets demonstrate that our proposed method in combination with an over-segmentation can provide high quality and competitive results compared to the existing state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "adaptive morphological reconstruction for seeded image segmentation"):
  with members:
    - "improved image segmentation via cost minimization of multiple hypotheses"
    - "revisiting graph construction for fast image segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "conditional random fields as recurrent neural networks"
    - "superpixel convolutional networks using bilateral inceptions"
    - "oriented edge forests for boundary detection"
    - "fast edge detection using structured forests"

Connected through hyperedge HE-2 (induced by source paper "structured group local sparse tracker"):
  with members:
    - "learning to track at 100 fps with deep regression networks"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-3 (induced by source paper "unsupervised image to image translation using domain specific variational information bound"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pixelnn example based image synthesis"
    - "infovae information maximizing variational autoencoders"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "unsupervised facial geometry learning for sketch to photo synthesis"):
  with members:
    - "a deep face identification network enhanced by facial attributes prediction"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "convolutional sketch inversion"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "image to image translation with conditional adversarial networks"
    - "colorful image colorization"

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)