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: "ds a flexible scalable and provably tight relaxation for matching problems"
  Abstract: Correspondence problems are often modelled as quadratic optimization problems over permutations. Common scalable methods for approximating solutions of these NP-hard problems are the spectral relaxation for non-convex energies and the doubly stochastic (DS) relaxation for convex energies. Lately, it has been demonstrated that semidefinite programming relaxations can have considerably improved accuracy at the price of a much higher computational cost. We present a convex quadratic programming relaxation which is provably stronger than both DS and spectral relaxations, with the same scalability as the DS relaxation. The derivation of the relaxation also naturally suggests a projection method for achieving meaningful integer solutions which improves upon the standard closest-permutation projection. Our method can be easily extended to optimization over doubly stochastic matrices, partial or injective matching, and problems with additional linear constraints. We employ recent advances in optimization of linear-assignment type problems to achieve an efficient algorithm for solving the convex relaxation. #R##N#We present experiments indicating that our method is more accurate than local minimization or competing relaxations for non-convex problems. We successfully apply our algorithm to shape matching and to the problem of ordering images in a grid, obtaining results which compare favorably with state of the art methods. We believe our results indicate that our method should be considered the method of choice for quadratic optimization over permutations.

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"
    - "a combinatorial solution to non rigid 3d shape to image matching"
    - "efficient globally optimal 2d to 3d deformable shape matching"
    - "alternating direction graph matching"

Connected through hyperedge HE-2 (induced by source paper "wasserstein based graph alignment"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "on the convergence of adam and beyond"
    - "netlsd hearing the shape of a graph"
    - "learning permutations with sinkhorn policy gradient"
    - "improved bilinear pooling with cnns"
    - "robust principal component analysis on graphs"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-3 (induced by source paper "efficient and robust shape correspondence via sparsity enforced quadratic assignment"):
  with members:
    - "self supervised learning of dense shape correspondence"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "graph matching relax or not"
    - "spectral generalized multi dimensional scaling"
    - "discrete laplace beltrami operator determines discrete riemannian metric"

Connected through hyperedge HE-4 (induced by source paper "synchronisation of partial multi matchings via non negative factorisations"):
  with members:
    - "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"
    - "a solution for multi alignment by transformation synchronisation"

Connected through hyperedge HE-5 (induced by source paper "got an optimal transport framework for graph comparison"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "on the convergence of adam and beyond"
    - "gromov wasserstein alignment of word embedding spaces"
    - "netlsd hearing the shape of a graph"
    - "implicit reparameterization gradients"
    - "learning permutations with sinkhorn policy gradient"
    - "improved bilinear pooling with cnns"
    - "network topology inference from spectral templates"

Connected through hyperedge HE-6 (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"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "alternating direction graph matching"
    - "outlier robust icp for minimizing fractional rmsd"

Connected through hyperedge HE-7 (induced by source paper "a genetic algorithm for fully automatic non isometric shape matching"):
  with members:
    - "continuous and orientation preserving correspondences via functional maps"
    - "reversible harmonic maps between discrete surfaces"

Connected through hyperedge HE-8 (induced by source paper "enigma evolutionary non isometric geometry matching"):
  with members:
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"

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.GR(Graphics)