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: "testing closeness with unequal sized samples"
  Abstract: We consider the problem of closeness testing for two discrete distributions in the practically relevant setting of \emph{unequal} sized samples drawn from each of them. Specifically, given a target error parameter $\varepsilon > 0$, $m_1$ independent draws from an unknown distribution $p,$ and $m_2$ draws from an unknown distribution $q$, we describe a test for distinguishing the case that $p=q$ from the case that $||p-q||_1 \geq \varepsilon$. If $p$ and $q$ are supported on at most $n$ elements, then our test is successful with high probability provided $m_1\geq n^{2/3}/\varepsilon^{4/3}$ and $m_2 = \Omega(\max\{\frac{n}{\sqrt m_1\varepsilon^2}, \frac{\sqrt n}{\varepsilon^2}\});$ we show that this tradeoff is optimal throughout this range, to constant factors. These results extend the recent work of Chan et al. who established the sample complexity when the two samples have equal sizes, and tightens the results of Acharya et al. by polynomials factors in both $n$ and $\varepsilon$. As a consequence, we obtain an algorithm for estimating the mixing time of a Markov chain on $n$ states up to a $\log n$ factor that uses $\tilde{O}(n^{3/2} \tau_{mix})$ queries to a "next node" oracle, improving upon the $\tilde{O}(n^{5/3}\tau_{mix})$ query algorithm of Batu et al. Finally, we note that the core of our testing algorithm is a relatively simple statistic that seems to perform well in practice, both on synthetic data and on natural language data.

Connected through hyperedge HE-1 (induced by source paper "private identity testing for high dimensional distributions"):
  with members:
    - "lower bounds for testing graphical models colorings and antiferromagnetic ising models"
    - "efficiently estimating erdos renyi graphs with node differential privacy"
    - "improved differentially private analysis of variance"
    - "individual sensitivity preprocessing for data privacy"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "learning and testing causal models with interventions"
    - "privately learning high dimensional distributions"
    - "test without trust optimal locally private distribution testing"

Connected through hyperedge HE-2 (induced by source paper "federated learning of n gram language models"):
  with members:
    - "approximating probabilistic models as weighted finite automata"
    - "federated learning strategies for improving communication efficiency"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "federated learning for mobile keyboard prediction"
    - "lstm a search space odyssey"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "learning differentially private recurrent language models"

Connected through hyperedge HE-3 (induced by source paper "sample amplification increasing dataset size even when learning is impossible"):
  with members:
    - "generative adversarial network in medical imaging a review"
    - "low shot learning from imaginary data"
    - "sampling correctors"
    - "a new approach for testing properties of discrete distributions"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "testing closeness of discrete distributions"

Connected through hyperedge HE-4 (induced by source paper "anaconda a non adaptive conditional sampling algorithm for distribution testing"):
  with members:
    - "generalized uniformity testing"
    - "which distribution distances are sublinearly testable"
    - "a new approach for testing properties of discrete distributions"
    - "testing ising models"
    - "improving and extending the testing of distributions for shape restricted properties"
    - "faster sublinear algorithms using conditional sampling"
    - "optimal testing for properties of distributions"
    - "big data on the rise testing monotonicity of distributions"

Connected through hyperedge HE-5 (induced by source paper "differentially private testing of identity and closeness of discrete distributions"):
  with members:
    - "differentially private identity and closeness testing of discrete distributions"
    - "optimal identity testing with high probability"
    - "generalized uniformity testing"
    - "a new approach for testing properties of discrete distributions"
    - "differentially private hypothesis testing revisited"
    - "testing identity of structured distributions"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "optimal algorithms for testing closeness of discrete distributions"

Connected through hyperedge HE-6 (induced by source paper "testing ising models"):
  with members:
    - "lower bounds for testing graphical models colorings and antiferromagnetic ising models"
    - "regression from dependent observations"
    - "learning from weakly dependent data under dobrushin s condition"
    - "private identity testing for high dimensional distributions"
    - "learning and testing causal models with interventions"
    - "sparse logistic regression learns all discrete pairwise graphical models"
    - "testing identity of multidimensional histograms"
    - "near optimal closeness testing of discrete histogram distributions"

Connected through hyperedge HE-7 (induced by source paper "recovering structured probability matrices"):
  with members:
    - "a latent variable model approach to pmi based word embeddings"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "learning mixtures of gaussians in high dimensions"
    - "random walks on context spaces towards an explanation of the mysteries of semantic word embeddings"
    - "exact recovery in the stochastic block model"
    - "spectral redemption in clustering sparse networks"
    - "efficient estimation of word representations in vector space"
    - "optimal algorithms for testing closeness of discrete distributions"

Connected through hyperedge HE-8 (induced by source paper "a new approach for testing properties of discrete distributions"):
  with members:
    - "optimal testing for properties of distributions"
    - "big data on the rise testing monotonicity of distributions"
    - "optimal algorithms and lower bounds for testing closeness of structured distributions"
    - "testing shape restrictions of discrete distributions"
    - "testing identity of structured distributions"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "testing k modal distributions optimal algorithms via reductions"
    - "testing closeness of discrete distributions"

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)