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 path following algorithm for the graph matching problem"
  Abstract: We propose a convex-concave programming approach for the labeled weighted graph matching problem. The convex-concave programming formulation is obtained by rewriting the weighted graph matching problem as a least-square problem on the set of permutation matrices and relaxing it to two different optimization problems: a quadratic convex and a quadratic concave optimization problem on the set of doubly stochastic matrices. The concave relaxation has the same global minimum as the initial graph matching problem, but the search for its global minimum is also a hard combinatorial problem. We therefore construct an approximation of the concave problem solution by following a solution path of a convex-concave problem obtained by linear interpolation of the convex and concave formulations, starting from the convex relaxation. This method allows to easily integrate the information on graph label similarities into the optimization problem, and therefore to perform labeled weighted graph matching. The algorithm is compared with some of the best performing graph matching methods on four datasets: simulated graphs, QAPLib, retina vessel images and handwritten chinese characters. In all cases, the results are competitive with the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "deep graph matching consensus"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "learning combinatorial embedding networks for deep graph matching"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "computing optimal assignments in linear time for approximate graph matching"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "deep adversarial network alignment"
    - "graph neural networks for user identity linkage"
    - "kergm kernelized graph matching"

Connected through hyperedge HE-2 (induced by source paper "probabilistic permutation synchronization using the riemannian structure of the birkhoff polytope"):
  with members:
    - "synchronisation of partial multi matchings via non negative factorisations"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc"
    - "averaging stochastic gradient descent on riemannian manifolds"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "cad priors for accurate and flexible instance reconstruction"
    - "consistent multiple graph matching with multi layer random walks synchronization"
    - "multi image semantic matching by mining consistent features"

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"
    - "alternating direction graph matching"

Connected through hyperedge HE-4 (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"
    - "a fast projected fixed point algorithm for large graph matching"
    - "point set registration coherent point drift"
    - "learning graph matching"

Connected through hyperedge HE-5 (induced by source paper "autoshufflenet learning permutation matrices via an exact lipschitz continuous penalty in deep convolutional neural networks"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "much faster algorithms for matrix scaling"
    - "convergence analysis of two layer neural networks with relu activation"

Connected through hyperedge HE-6 (induced by source paper "kergm kernelized graph matching"):
  with members:
    - "scalable global alignment graph kernel using random features from node embedding to graph embedding"
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "triangular alignment tame a tensor based approach for higher order network alignment"
    - "stable and informative spectral signatures for graph matching"
    - "subgraph matching kernels for attributed graphs"

Connected through hyperedge HE-7 (induced by source paper "regal representation learning based graph alignment"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "learning structural node embeddings via diffusion wavelets"
    - "struc2vec learning node representations from structural identity"
    - "node2vec scalable feature learning for networks"
    - "role discovery in networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "deltacon a principled massive graph similarity function"

Connected through hyperedge HE-8 (induced by source paper "unsupervised alignment of embeddings with wasserstein procrustes"):
  with members:
    - "an iterative closest point method for unsupervised word translation"
    - "non adversarial unsupervised word translation"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"
    - "improving zero shot learning by mitigating the hubness problem"
    - "graph matching relax or not"

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)