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: "efficient and non convex coordinate descent for symmetric nonnegative matrix factorization"
  Abstract: Given a symmetric nonnegative matrix   $A$  , symmetric nonnegative matrix factorization (symNMF) is the problem of finding a nonnegative matrix    $H$  , usually with much fewer columns than    $A$  , such that   $A \approx HH^T$  . SymNMF can be used for data analysis and in particular for various clustering tasks. Unlike standard NMF, which is traditionally solved by a series of quadratic (convex) subproblems, we propose to solve symNMF by directly solving the nonconvex problem, namely, minimize    $\Vert A-HH^T\Vert ^2$  , which is a fourth-order nonconvex problem. In this paper, we propose simple and very efficient coordinate descent schemes, which solve a series of fourth-order univariate subproblems exactly. We also derive convergence guarantees for our methods and show that they perform favorably compared to recent state-of-the-art methods on synthetic and real-world datasets, especially on large and sparse input matrices.

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"
    - "a constrained clustering based approach for matching a collection of feature sets"
    - "spectral synchronization of multiple views in se 3"
    - "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 "accelerating nonnegative matrix factorization algorithms using extrapolation"):
  with members:
    - "introduction to nonnegative matrix factorization"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-3 (induced by source paper "a decomposition algorithm for sparse generalized eigenvalue problem"):
  with members:
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "incremental training of a detector using online sparse eigendecomposition"
    - "efficiently learning a detection cascade with sparse eigenvectors"
    - "optimal solutions for sparse principal component analysis"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-4 (induced by source paper "dropping symmetry for fast symmetric nonnegative matrix factorization"):
  with members:
    - "the global optimization geometry of low rank matrix optimization"

Connected through hyperedge HE-5 (induced by source paper "similarity preserving representation learning for time series clustering"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "matrix completion has no spurious local minimum"
    - "deep learning in neural networks"
    - "highly comparative feature based time series classification"
    - "a time series forest for classification and feature extraction"
    - "voice recognition algorithms using mel frequency cepstral coefficient mfcc and dynamic time warping dtw techniques"

Connected through hyperedge HE-6 (induced by source paper "frank wolfe optimization for symmetric nmf under simplicial constraint"):
  with members:
    - "block coordinate frank wolfe optimization for structural svms"
    - "on the complexity of nonnegative matrix factorization"

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.NA(Numerical Analysis)