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: "basic thresholding classification"
  Abstract: In this thesis, we propose a light-weight sparsity-based algorithm, basic thresholding classifier (BTC), for classification applications (such as face identification, hyper-spectral image classification, etc.) which is capable of identifying test samples extremely rapidly and performing high classification accuracy. Originally BTC is a linear classifier which works based on the assumption that the samples of the classes of a given dataset are linearly separable. However, in practice those samples may not be linearly separable. In this context, we also propose another algorithm namely kernel basic thresholding classifier (KBTC) which is a non-linear kernel version of the BTC algorithm. KBTC can achieve promising results especially when the given samples are linearly non-separable. For both proposals, we introduce sufficient identification conditions (SICs) under which BTC and KBTC can identify any test sample in the range space of a given dictionary. By using SICs, we develop parameter estimation procedures which do not require any cross validation. Both BTC and KBTC algorithms provide efficient classifier fusion schemes in which individual classifier outputs are combined to produce better classification results. For instance, for the application of face identification, this is done by combining the residuals having different random projectors. For spatial applications such as hyper-spectral image classification, the fusion is carried out by incorporating the spatial information, in which the output residual maps are filtered using a smoothing filter. Numerical results on publicly available face and hyper-spectral datasets show that our proposal outperforms well-known support vector machines (SVM)-based techniques, multinomial logistic regression (MLR)-based methods, and sparsity-based approaches like $l_1$-minimization and simultaneous orthogonal matching pursuit (SOMP).

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)