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: "algorithms initializations and convergence for the nonnegative matrix factorization"
  Abstract: It is well known that good initializations can improve the speed and accuracy of the solutions of many nonnegative matrix factorization (NMF) algorithms. Many NMF algorithms are sensitive with respect to the initialization of W or H or both. This is especially true of algorithms of the alternating least squares (ALS) type, including the two new ALS algorithms that we present in this paper. We compare the results of six initialization procedures (two standard and four new) on our ALS algorithms. Lastly, we discuss the practical issue of choosing an appropriate convergence criterion.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving multiple tensor factorization for synthesizing large scale location traces"):
  with members:
    - "local obfuscation mechanisms for hiding probability distributions"
    - "on the lack of location privacy in crowdsourcing applications"
    - "the long road to computational location privacy a survey"
    - "evaluating differentially private machine learning in practice"
    - "utility optimized local differential privacy mechanisms for distribution estimation"
    - "plausible deniability for privacy preserving data synthesis"
    - "fast differentially private matrix factorization"
    - "time distortion anonymization for the publication of mobility data with high utility"

Connected through hyperedge HE-2 (induced by source paper "supervised nonnegative matrix factorization to predict icu mortality risk"):
  with members:
    - "non negative matrix factorization with sparseness constraints"
    - "non negative sparse coding"

Connected through hyperedge HE-3 (induced by source paper "data fusion by matrix factorization"):
  with members:
    - "a bayesian matrix factorization model for relational data"

Connected through hyperedge HE-4 (induced by source paper "a field guide to forward backward splitting with a fasta implementation"):
  with members:
    - "fast approximations to structured sparse coding and applications to object classification"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "anti sparse coding for approximate nearest neighbor search"
    - "low rank matrix completion with noisy observations a quantitative comparison"

Connected through hyperedge HE-5 (induced by source paper "pattern decomposition with complex combinatorial constraints application to materials discovery"):
  with members:
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-6 (induced by source paper "algorithms for internal validation clustering measures in the post genomic era"):
  with members:
    - "on low distortion embeddings of statistical distance measures into low dimensional spaces"
    - "non negative matrix factorization with sparseness constraints"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-7 (induced by source paper "descent methods for nonnegative matrix factorization"):
  with members:
    - "nonnegative matrix factorization via rank one downdate"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-8 (induced by source paper "clustering and latent semantic indexing aspects of the nonnegative matrix factorization"):
  with members:
    - "non negative matrix factorization with sparseness constraints"

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)