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: "dfdl discriminative feature oriented dictionary learning for histopathological image classification"
  Abstract: In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structure. In this paper, we propose an automatic feature discovery framework for extracting discriminative class-specific features and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific features which are suitable for representing samples from the same class while are poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian lung images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, show the significance of DFDL model in a variety problems over state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "signal classification under structured sparsity constraints"):
  with members:
    - "fast low rank shared dictionary learning for image classification"
    - "learning a low rank shared dictionary for object classification"
    - "adaptive matching pursuit for sparse signal recovery"
    - "patch based convolutional neural network for whole slide tissue image classification"

Connected through hyperedge HE-2 (induced by source paper "classification of histopathological breast cancer images using iterative vmd aided zernike moments textural signatures"):
  with members:
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "enhanced signal recovery via sparsity inducing image priors"):
  with members:
    - "sparsity based color image super resolution via exploiting cross channel constraints"
    - "deep convolutional neural network for inverse problems in imaging"
    - "accelerating the super resolution convolutional neural network"
    - "detecting dominant vanishing points in natural scenes with application to composition sensitive image retrieval"
    - "screen content image segmentation using sparse smooth decomposition"
    - "accurate image super resolution using very deep convolutional networks"
    - "self tuned deep super resolution"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "fast low rank shared dictionary learning for image classification"):
  with members:
    - "learning a low rank shared dictionary for object classification"
    - "adaptive matching pursuit for sparse signal recovery"
    - "task driven dictionary learning for hyperspectral image classification with structured sparsity constraints"
    - "multi task image classification via collaborative hierarchical spike and slab priors"
    - "collaborative multi sensor classification via sparsity based representation"
    - "structured priors for sparse representation based hyperspectral image classification"

Connected through hyperedge HE-5 (induced by source paper "robust sonar atr through bayesian pose corrected sparse classification"):
  with members:
    - "localized dictionary design for geometrically robust sonar atr"
    - "discriminative sparsity for sonar atr"
    - "a survey of sparse representation algorithms and applications"
    - "graph based classification of multiple observation sets"

Connected through hyperedge HE-6 (induced by source paper "adaptive matching pursuit for sparse signal recovery"):
  with members:
    - "learning a low rank shared dictionary for object classification"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

Connected through hyperedge HE-7 (induced by source paper "learning a low rank shared dictionary for object classification"):
  with members:
    - "multi task image classification via collaborative hierarchical spike and slab priors"

Connected through hyperedge HE-8 (induced by source paper "localized dictionary design for geometrically robust sonar atr"):
  with members:
    - "discriminative sparsity for sonar atr"

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)