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: "secrets of grabcut and kernel k means"
  Abstract: The log-likelihood energy term in popular model-fitting segmentation methods, e.g. Zhu-Yuille, Chan-Vese, GrabCut, etc., is presented as a generalized "probabilistic" K-means energy for color space clustering. This interpretation reveals some limitations, e.g. over-fitting. We propose an alternative approach to color clustering using kernel K-means energy with well-known properties such as non-linear separation and scalability to higher-dimensional feature spaces. Similarly to log-likelihoods, our kernel energy term for color space clustering can be combined with image grid regularization, e.g. boundary smoothness, and minimized using (pseudo-) bound optimization and max-flow algorithm. Unlike histogram or GMM fitting and implicit entropy minimization, our approach is closely related to general pairwise clustering such as average association and normalized cut. But, in contrast to previous pairwise clustering algorithms, our approach can incorporate any standard geometric regularization in the image domain. We analyze extreme cases for kernel bandwidth (e.g. Gini bias) and propose adaptive strategies. Our general kernel-based approach opens the door for many extensions/applications.

Connected through hyperedge HE-1 (induced by source paper "deep extreme cut from extreme points to object segmentation"):
  with members:
    - "deep grabcut for object selection"
    - "the 2017 davis challenge on video object segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "training object class detectors with click supervision"
    - "click carving segmenting objects in video with point clicks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "what makes for effective detection proposals"

Connected through hyperedge HE-2 (induced by source paper "deep grabcut for object selection"):
  with members:
    - "deep interactive object selection"
    - "iterative instance segmentation"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "learning deconvolution network for semantic segmentation"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "convolutional feature masking for joint object and stuff segmentation"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "discrete continuous admm for transductive inference in higher order mrfs"):
  with members:
    - "analysis and optimization of loss functions for multiclass top k and multilabel classification"
    - "a message passing algorithm for the minimum cost multicut problem"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "a new look at reweighted message passing"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "a convex relaxation for weakly supervised classifiers"

Connected through hyperedge HE-4 (induced by source paper "dope distributed optimization for pairwise energies"):
  with members:
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"
    - "principled parallel mean field inference for discrete random fields"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-5 (induced by source paper "simple does it weakly supervised instance and semantic segmentation"):
  with members:
    - "stc a simple to complex framework for weakly supervised semantic 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"
    - "learning to refine object segments"
    - "what makes for effective detection proposals"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "deep interactive object selection"
    - "loosecut interactive image segmentation with loosely bounded boxes"

Connected through hyperedge HE-6 (induced by source paper "weakly supervised semantic labelling and instance segmentation"):
  with members:
    - "multi scale context aggregation by dilated convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "kernel cuts mrf meets kernel spectral clustering"):
  with members:
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "volumetric bias in segmentation and reconstruction secrets and solutions"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "scalable kernel clustering approximate kernel k means"
    - "an information theoretic analysis of hard and soft assignment methods for clustering"
    - "the k modes algorithm for clustering"
    - "efficient inference in fully connected crfs with gaussian edge potentials"
    - "a submodular supermodular procedure with applications to discriminative structure learning"

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)