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: "how does the low rank matrix decomposition help internal and external learnings for super resolution"
  Abstract: A new challenge in Super-resolution (SR) problem is how to utilize the pros of internal and external learnings to further enhance the result. To address this issue, we analyze the attributes of these two kinds of methods, and find they are complementary in the feature space and image plan, meanwhile, have sparse estimation error. This finding inspires us to propose a low-rank solution which effectively integrates them together. We then tailor an internal prior learning and an external dictionary learning to fit the solution. With a theoretical analysis on the algorithm, we also prove that the low-rank solution does not require massive input to guarantee the performance. This simplifies the design of the internal and external learning methods for the solution, and reduces the computation cost. Unlike other methods, the proposed solution is a parameter free integration, and can be generalized with more recent internal and external learning methods. Intensive experiments show the proposed solution reconstructs image details effectively, also outperforms state-of-the-arts in both visual and quantitative assessments, especially for the noisy images.

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)