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: "alternating direction graph matching"
  Abstract: In this paper, we introduce a graph matching method that can account for constraints of arbitrary order, with arbitrary potential functions. Unlike previous decomposition approaches that rely on the graph structures, we introduce a decomposition of the matching constraints. Graph matching is then reformulated as a non-convex non-separable optimization problem that can be split into smaller and much-easier-to-solve subproblems, by means of the alternating direction method of multipliers. The proposed framework is modular, scalable, and can be instantiated into different variants. Two instantiations are studied exploring pairwise and higher-order constraints. Experimental results on widely adopted benchmarks involving synthetic and real examples demonstrate that the proposed solutions outperform existing pairwise graph matching methods, and competitive with the state of the art in higher-order settings.

Connected through hyperedge HE-1 (induced by source paper "mina convex mixed integer programming for non rigid shape alignment"):
  with members:
    - "zoomout spectral upsampling for efficient shape correspondence"
    - "continuous and orientation preserving correspondences via functional maps"
    - "unsupervised deep learning for structured shape matching"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "a combinatorial solution to non rigid 3d shape to image matching"
    - "efficient globally optimal 2d to 3d deformable shape matching"

Connected through hyperedge HE-2 (induced by source paper "graph based method for anomaly detection in functional brain network using variational autoencoder"):
  with members:
    - "adaptively transforming graph matching"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "large scale learnable graph convolutional networks"
    - "spherical latent spaces for stable variational autoencoders"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "semi supervised hierarchical semantic object parsing"

Connected through hyperedge HE-3 (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"
    - "a constrained clustering based approach for matching a collection of feature sets"
    - "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"
    - "a solution for multi alignment by transformation synchronisation"

Connected through hyperedge HE-4 (induced by source paper "a functional representation for graph matching"):
  with members:
    - "adaptively transforming graph matching"
    - "optimal transport for domain adaptation"
    - "a fast projected fixed point algorithm for large graph matching"
    - "point set registration coherent point drift"
    - "a path following algorithm for the graph matching problem"
    - "learning graph matching"

Connected through hyperedge HE-5 (induced by source paper "sdrsac semidefinite based randomized approach for robust point cloud registration without correspondences"):
  with members:
    - "on device scalable image based localization via prioritized cascade search and fast one many ransac"
    - "deterministic consensus maximization with biconvex programming"
    - "maximum consensus parameter estimation by reweighted ell_1 methods"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "outlier robust icp for minimizing fractional rmsd"

Connected through hyperedge HE-6 (induced by source paper "continuous relaxation of map inference a nonconvex perspective"):
  with members:
    - "a new look at reweighted message passing"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "efficient mrf energy minimization via adaptive diminishing smoothing"
    - "opengm a c library for discrete graphical models"
    - "efficiently searching for frustrated cycles in map inference"

Connected through hyperedge HE-7 (induced by source paper "adaptively transforming graph matching"):
  with members:
    - "anisotropic scale junction detection and matching for indoor images"
    - "optimal transport for domain adaptation"
    - "a path following algorithm for the graph matching problem"
    - "learning graph matching"

Connected through hyperedge HE-8 (induced by source paper "unsupervised domain adaptation using regularized hyper graph matching"):
  with members:
    - "return of frustratingly easy domain adaptation"
    - "optimal transport for domain adaptation"

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)