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: "why are big data matrices approximately low rank"
  Abstract: Matrices of (approximate) low rank are pervasive in data science, appearing in recommender systems, movie preferences, topic models, medical records, and genomics. While there is a vast literature on how to exploit low rank structure in these datasets, there is less attention on explaining why the low rank structure appears in the first place. Here, we explain the effectiveness of low rank models in data science by considering a simple generative model for these matrices: we suppose that each row or column is associated to a (possibly high dimensional) bounded latent variable, and entries of the matrix are generated by applying a piecewise analytic function to these latent variables. These matrices are in general full rank. However, we show that we can approximate every entry of an $m \times n$ matrix drawn from this model to within a fixed absolute error by a low rank matrix whose rank grows as $\mathcal O(\log(m + n))$. Hence any sufficiently large matrix from such a latent variable model can be approximated, up to a small entrywise error, by a low rank matrix.

Connected through hyperedge HE-1 (induced by source paper "on robustness of principal component regression"):
  with members:
    - "harnessing structures for value based planning and reinforcement learning"
    - "model agnostic time series analysis via matrix estimation"
    - "nice latent variable models have log rank"
    - "bayesian estimation from few samples community detection and related problems"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from noisy entries"
    - "matrix completion from a few entries"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-2 (induced by source paper "oboe collaborative filtering for automl model selection"):
  with members:
    - "cf4cf recommending collaborative filtering algorithms using collaborative filtering"
    - "autostacker a compositional evolutionary learning system"
    - "hyperparameter optimization a spectral approach"
    - "flash fast bayesian optimization for data analytic pipelines"
    - "openml networked science in machine learning"
    - "algorithm runtime prediction methods evaluation"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-3 (induced by source paper "polynomial matrix completion for missing data imputation and transductive learning"):
  with members:
    - "guaranteed matrix completion via non convex factorization"
    - "adam a method for stochastic optimization"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "sparse subspace clustering algorithm theory and applications"
    - "high rank matrix completion and subspace clustering with missing data"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "bigmap big data mapping with parallelized t sne"):
  with members:
    - "fast interpolation based t sne for improved visualization of single cell rna seq data"
    - "an analysis of the t sne algorithm for data visualization"
    - "t sne cuda gpu accelerated t sne and its applications to modern data"
    - "clustering with t sne provably"
    - "approximated and user steerable tsne for progressive visual analytics"

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.LG(Machine Learning)