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: "object learning and convex cardinal shape composition"
  Abstract: This work mainly focuses on a novel segmentation and partitioning scheme, based on learning the principal elements of the optimal partitioner in the image. The problem of interest is characterizing the objects present in an image as a composition of matching elements from a dictionary of prototype shapes. The composition model allows set union and difference among the selected elements, while regularizing the problem by restricting their count to a fixed level. This is a combinatorial problem addressing which is not in general computationally tractable. Convex cardinal shape composition (CSC) is a recent relaxation scheme presented as a proxy to the original problem. From a theoretical standpoint, this paper improves the results presented in the original work, by deriving the general sufficient conditions under which CSC identifies a target composition. We also provide qualitative results on who well the CSC outcome approximates the combinatorial solution. From a computational standpoint, two convex solvers, one supporting distributed processing for large-scale problems, and one cast as a linear program are presented. Applications such as multi-resolution segmentation and recovery of the principal shape components are presented as the experiments supporting the proposed ideas.

Connected through hyperedge HE-1 (induced by source paper "sweep distortion removal from thz images via blind demodulation"):
  with members:
    - "near optimal compressed sensing of a class of sparse low rank matrices via sparse power factorization"
    - "near optimal compressed sensing of sparse rank one matrices via sparse power factorization"

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)