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: "target based hyperspectral demixing via generalized robust pca"
  Abstract: Localizing targets of interest in a given hyperspectral (HS) image has applications ranging from remote sensing to surveillance. This task of target detection leverages the fact that each material/object possesses its own characteristic spectral response, depending upon its composition. As signatures of different materials are often correlated, matched filtering based approaches may not be appropriate in this case. In this work, we present a technique to localize targets of interest based on their spectral signatures. We also present the corresponding recovery guarantees, leveraging our recent theoretical results. To this end, we model a HS image as a superposition of a low-rank component and a dictionary sparse component, wherein the dictionary consists of the a priori known characteristic spectral responses of the target we wish to localize. Finally, we analyze the performance of the proposed approach via experimental validation on real HS data for a classification task, and compare it with related techniques.

Connected through hyperedge HE-1 (induced by source paper "noodl provable online dictionary learning and sparse coding"):
  with members:
    - "a dictionary based generalization of robust pca part ii applications to hyperspectral demixing"
    - "a dictionary based generalization of robust pca part i study of theoretical properties"
    - "stochastic variance reduced optimization for nonconvex sparse learning"
    - "a dictionary based generalization of robust pca"
    - "symmetry saddle points and global geometry of nonconvex matrix factorization"
    - "simple efficient and neural algorithms for sparse coding"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "dictionary learning and tensor decomposition via the sum of squares method"

Connected through hyperedge HE-2 (induced by source paper "a dictionary based generalization of robust pca part ii applications to hyperspectral demixing"):
  with members:
    - "a dictionary based generalization of robust pca"
    - "simultaneously sparse and low rank abundance matrix estimation for hyperspectral image unmixing"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "semi blind source separation via sparse representations and online dictionary learning"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "compressive principal component pursuit"
    - "low rank matrix recovery from errors and erasures"
    - "stable principal component pursuit"

Connected through hyperedge HE-3 (induced by source paper "a dictionary based generalization of robust pca part i study of theoretical properties"):
  with members:
    - "a dictionary based generalization of robust pca"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "compressive principal component pursuit"
    - "low rank matrix recovery from errors and erasures"
    - "stable principal component pursuit"
    - "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)