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: "superpixel sampling networks"
  Abstract: Superpixels provide an efficient low/mid-level representation of image data, which greatly reduces the number of image primitives for subsequent vision tasks. Existing superpixel algorithms are not differentiable, making them difficult to integrate into otherwise end-to-end trainable deep neural networks. We develop a new differentiable model for superpixel sampling that leverages deep networks for learning superpixel segmentation. The resulting "Superpixel Sampling Network" (SSN) is end-to-end trainable, which allows learning task-specific superpixels with flexible loss functions and has fast runtime. Extensive experimental analysis indicates that SSNs not only outperform existing superpixel algorithms on traditional segmentation benchmarks, but can also learn superpixels for other tasks. In addition, SSNs can be easily integrated into downstream deep networks resulting in performance improvements.

Connected through hyperedge HE-1 (induced by source paper "superpixel segmentation via convolutional neural networks with regularized information maximization"):
  with members:
    - "deep image prior"
    - "instance normalization the missing ingredient for fast stylization"
    - "unsupervised monocular depth estimation with left right consistency"
    - "adam a method for stochastic optimization"
    - "scikit image image processing in python"

Connected through hyperedge HE-2 (induced by source paper "point cloud oversegmentation with graph structured deep metric learning"):
  with members:
    - "clustering with deep learning taxonomy and new methods"
    - "group normalization"
    - "superpixels an evaluation of the state of the art"
    - "know what your neighbors do 3d semantic segmentation of point clouds"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "graph based over segmentation methods for 3d point clouds"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"

Connected through hyperedge HE-3 (induced by source paper "supervized segmentation with graph structured deep metric learning"):
  with members:
    - "point cloud oversegmentation with graph structured deep metric learning"
    - "group normalization"
    - "know what your neighbors do 3d semantic segmentation of point clouds"
    - "exploring spatial context for 3d semantic segmentation of point clouds"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "spatial transformer networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "a novel centroid update approach for clustering based superpixel method and superpixel based edge detection"):
  with members:
    - "superpixels an evaluation of the state of the art"

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)