human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "clustered multitask nonnegative matrix factorization for spectral unmixing of hyperspectral data"):
  Abstract: In this paper, the new algorithm based on clustered multitask network is proposed to solve spectral unmixing problem in hyperspectral imagery. In the proposed algorithm, the clustered network is employed. Each pixel in the hyperspectral image considered as a node in this network. The nodes in the network are clustered using the fuzzy c-means clustering method. Diffusion least mean square strategy has been used to optimize the proposed cost function. To evaluate the proposed method, experiments are conducted on synthetic and real datasets. Simulation results based on spectral angle distance, abundance angle distance and reconstruction error metrics illustrate the advantage of the proposed algorithm compared with other methods.

Includes members:
  Member "sparsity constrained distributed unmixing of hyperspectral data":
    also connected through other hyperedges:
      (none)
  Member "hyperspectral unmixing based on clustered multitask networks":
    also connected through other hyperedges:
      (none)
  Member "spectral unmixing of hyperspectral imagery using multilayer nmf":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep self representative concept factorization network for representation learning")
  Member "structured sparse method for hyperspectral unmixing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "low rank tensor grid for image completion")
  Member "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "study of robust distributed diffusion rls algorithms with side information for adaptive networks")
  Member "hyperspectral data unmixing using gnmf method and sparseness constraint":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed unmixing of hyperspectral data with sparsity constraint")
  Member "multitask diffusion adaptation over networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis")

Question: Please tell me which class the source paper 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)