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 fast projected fixed point algorithm for large graph matching"
  Abstract: We propose a fast approximate algorithm for large graph matching. A new projected fixed-point method is defined and a new doubly stochastic projection is adopted to derive the algorithm. Previous graph matching algorithms suffer from high computational complexity and therefore do not have good scalability with respect to graph size. For matching two weighted graphs of $n$ nodes, our algorithm has time complexity only $O(n^3)$ per iteration and space complexity $O(n^2)$. In addition to its scalability, our algorithm is easy to implement, robust, and able to match undirected weighted attributed graphs of different sizes. While the convergence rate of previous iterative graph matching algorithms is unknown, our algorithm is theoretically guaranteed to converge at a linear rate. Extensive experiments on large synthetic and real graphs (more than 1,000 nodes) were conducted to evaluate the performance of various algorithms. Results show that in most cases our proposed algorithm achieves better performance than previous state-of-the-art algorithms in terms of both speed and accuracy in large graph matching. In particular, with high accuracy, our algorithm takes only a few seconds (in a PC) to match two graphs of 1,000 nodes.

Connected through hyperedge HE-1 (induced by source paper "a functional representation for graph matching"):
  with members:
    - "adaptively transforming graph matching"
    - "alternating direction graph matching"
    - "optimal transport for domain adaptation"
    - "point set registration coherent point drift"
    - "a path following algorithm for the graph matching problem"
    - "learning graph matching"

Connected through hyperedge HE-2 (induced by source paper "deepnc deep generative network completion"):
  with members:
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "community detection in partially observable social networks"
    - "learning deep generative models of graphs"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "multi image semantic matching by mining consistent features"):
  with members:
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "proposal flow semantic correspondences from object proposals"
    - "unsupervised learning of object landmarks by factorized spatial embeddings"
    - "lift learned invariant feature transform"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "multi graph matching via affinity optimization with graduated consistency regularization"
    - "learning dense correspondence via 3d guided cycle consistency"
    - "learning covariant feature detectors"

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)