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: "thesis multiple kernel learning for object categorization"
  Abstract: Object Categorization is a challenging problem, especially when the images have clutter background, occlusions or different lighting conditions. In the past, many descriptors have been proposed which aid object categorization even in such adverse conditions. Each descriptor has its own merits and de-merits. Some descriptors are invariant to transformations while the others are more discriminative. Past research has shown that, employing multiple descriptors rather than any single descriptor leads to better recognition. The problem of learning the optimal combination of the available descriptors for a particular classification task is studied. Multiple Kernel Learning (MKL) framework has been developed for learning an optimal combination of descriptors for object categorization. Existing MKL formulations often employ block l-1 norm regularization which is equivalent to selecting a single kernel from a library of kernels. Since essentially a single descriptor is selected, the existing formulations maybe sub- optimal for object categorization. A MKL formulation based on block l-infinity norm regularization has been developed, which chooses an optimal combination of kernels as opposed to selecting a single kernel. A Composite Multiple Kernel Learning(CKL) formulation based on mixed l-infinity and l-1 norm regularization has been developed. These formulations end in Second Order Cone Programs(SOCP). Other efficient alter- native algorithms for these formulation have been implemented. Empirical results on benchmark datasets show significant improvement using these new MKL formulations.

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)