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: "multi task image classification via collaborative hierarchical spike and slab priors"
  Abstract: Promising results have been achieved in image classification problems by exploiting the discriminative power of sparse representations for classification (SRC). Recently, it has been shown that the use of \emph{class-specific} spike-and-slab priors in conjunction with the class-specific dictionaries from SRC is particularly effective in low training scenarios. As a logical extension, we build on this framework for multitask scenarios, wherein multiple representations of the same physical phenomena are available. We experimentally demonstrate the benefits of mining joint information from different camera views for multi-view face recognition.

Connected through hyperedge HE-1 (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"
    - "dfdl discriminative feature oriented dictionary learning for histopathological image classification"
    - "task driven dictionary learning for hyperspectral image classification with structured sparsity constraints"
    - "collaborative multi sensor classification via sparsity based representation"
    - "structured priors for sparse representation based hyperspectral image classification"

Connected through hyperedge HE-2 (induced by source paper "learning a low rank shared dictionary for object classification"):
  with members:
    - "dfdl discriminative feature oriented dictionary learning for histopathological image classification"

Connected through hyperedge HE-3 (induced by source paper "fingerprint recognition using translation invariant scattering network"):
  with members:
    - "screen content image segmentation using sparse smooth decomposition"
    - "iris recognition using scattering transform and textural features"
    - "screen content image segmentation using least absolute deviation fitting"
    - "multispectral palmprint recognition using textural features"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"
    - "invariant scattering convolution networks"
    - "classification with scattering operators"

Connected through hyperedge HE-4 (induced by source paper "dfdl discriminative feature oriented dictionary learning for histopathological image classification"):
  with members:
    - "quality based multimodal classification using tree structured sparsity"

Connected through hyperedge HE-5 (induced by source paper "on the power of joint wavelet dct features for multispectral palmprint recognition"):
  with members:
    - "iris recognition using scattering transform and textural features"
    - "multispectral palmprint recognition using textural features"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"
    - "multispectral palmprint recognition using a hybrid feature"
    - "a geometric approach for fully automatic chromosome segmentation"

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)