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: "collaborative multi sensor classification via sparsity based representation"
  Abstract: In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification, which takes into account the correlations as well as complementary information between heterogeneous sensors simultaneously while considering joint sparsity within each sensor's observations. We also robustify our models to deal with the presence of sparse noise and low-rank interference signals. Specifically, we demonstrate that incorporating the noise or interference signal as a low-rank component in our models is essential in a multi-sensor classification problem when multiple co-located sources/sensors simultaneously record the same physical event. We further extend our frameworks to kernelized models which rely on sparsely representing a test sample in terms of all the training samples in a feature space induced by a kernel function. A fast and efficient algorithm based on alternative direction method is proposed where its convergence to an optimal solution is guaranteed. Extensive experiments are conducted on several real multi-sensor data sets and results are compared with the conventional classifiers to verify the effectiveness of the proposed methods.

Connected through hyperedge HE-1 (induced by source paper "multi modal image processing based on coupled dictionary learning"):
  with members:
    - "multi modal dictionary learning for image separation with application in art investigation"
    - "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"
    - "x ray image separation via coupled dictionary learning"
    - "hyperspectral pansharpening a review"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-2 (induced by source paper "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"):
  with members:
    - "multi modal dictionary learning for image separation with application in art investigation"
    - "accelerating the super resolution convolutional neural network"
    - "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"
    - "x ray image separation via coupled dictionary learning"
    - "accurate image super resolution using very deep convolutional networks"
    - "hyperspectral pansharpening a review"
    - "deeply recursive convolutional network for image super resolution"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-3 (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"
    - "multi task image classification via collaborative hierarchical spike and slab priors"
    - "structured priors for sparse representation based hyperspectral image classification"

Connected through hyperedge HE-4 (induced by source paper "linear disentangled representation learning for facial actions"):
  with members:
    - "pose selective max pooling for measuring similarity"
    - "hierarchical sparse and collaborative low rank representation for emotion recognition"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "reliable recovery of hierarchically sparse signals and application in machine type communications"):
  with members:
    - "low rank tensor recovery via iterative hard thresholding"
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "structured sparsity through convex optimization"
    - "model based compressive sensing"
    - "collaborative hierarchical sparse modeling"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-6 (induced by source paper "hierarchical sparse and collaborative low rank representation for emotion recognition"):
  with members:
    - "structured dictionary learning for classification"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "the projected gsure for automatic parameter tuning in iterative shrinkage methods"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "robust principal component analysis"

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)