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: "dynamical spectral unmixing of multitemporal hyperspectral images"
  Abstract: In this paper, we consider the problem of unmixing a time series of hyperspectral images. We propose a dynamical model based on linear mixing processes at each time instant. The spectral signatures and fractional abundances of the pure materials in the scene are seen as latent variables, and assumed to follow a general dynamical structure. Based on a simplified version of this model, we derive an efficient spectral unmixing algorithm to estimate the latent variables by performing alternating minimizations. The performance of the proposed approach is demonstrated on synthetic and real multitemporal hyperspectral images.

Connected through hyperedge HE-1 (induced by source paper "orsim detector a novel object detection framework in optical remote sensing imagery using spatial frequency channel features"):
  with members:
    - "learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification"
    - "cospace common subspace learning from hyperspectral multispectral correspondences"
    - "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"
    - "joint progressive learning from high dimensional data for multi label classification"
    - "a survey on object detection in optical remote sensing images"
    - "yolo9000 better faster stronger"
    - "aggregate channel features for multi view face detection"

Connected through hyperedge HE-2 (induced by source paper "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"):
  with members:
    - "joint progressive learning from high dimensional data for multi label classification"
    - "structured sparse method for hyperspectral unmixing"
    - "robust hyperspectral unmixing with correntropy based metric"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"

Connected through hyperedge HE-3 (induced by source paper "cospace common subspace learning from hyperspectral multispectral correspondences"):
  with members:
    - "learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification"
    - "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"
    - "joint progressive learning from high dimensional data for multi label classification"

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)