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: "a constrained clustering based approach for matching a collection of feature sets"
  Abstract: In this paper, we consider the problem of finding the feature correspondences among a collection of feature sets, by using their point-wise unary features. This is a fundamental problem in computer vision and pattern recognition, which also closely relates to other areas such as operational research. Different from two-set matching which can be transformed to a quadratic assignment programming task that is known NP-hard, inclusion of merely unary attributes leads to a linear assignment problem for matching two feature sets. This problem has been well studied and there are effective polynomial global optimum solvers such as the Hungarian method. However, it becomes ill-posed when the unary attributes are (heavily) corrupted. The global optimal correspondence concerning the best score defined by the attribute affinity/cost between the two sets can be distinct to the ground truth correspondence since the score function is biased by noises. To combat this issue, we devise a method for matching a collection of feature sets by synergetically exploring the information across the sets. In general, our method can be perceived from a (constrained) clustering perspective: in each iteration, it assigns the features of one set to the clusters formed by the rest of feature sets, and updates the cluster centers in turn. Results on both synthetic data and real images suggest the efficacy of our method against state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "synchronisation of partial multi matchings via non negative factorisations"):
  with members:
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "distributable consistent multi object matching"
    - "spectral synchronization of multiple views in se 3"
    - "efficient and non convex coordinate descent for symmetric nonnegative matrix factorization"
    - "multi graph matching via affinity optimization with graduated consistency regularization"
    - "a dual ascent framework for lagrangean decomposition of combinatorial problems"
    - "alternating direction graph matching"
    - "a solution for multi alignment by transformation synchronisation"

Connected through hyperedge HE-2 (induced by source paper "clear a consistent lifting embedding and alignment rectification algorithm for multi agent data association"):
  with members:
    - "synchronisation of partial multi matchings via non negative factorisations"
    - "all graphs lead to rome learning geometric and cycle consistent representations with graph convolutional networks"
    - "multi image semantic matching by mining consistent features"
    - "distributable consistent multi object matching"
    - "multi graph matching via affinity optimization with graduated consistency regularization"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "multi image matching via fast alternating minimization"
    - "near optimal joint object matching via convex relaxation"

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)