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: "optimal algorithms for testing closeness of discrete distributions"
  Abstract: We study the question of closeness testing for two discrete distributions. More precisely, given samples from two distributions $p$ and $q$ over an $n$-element set, we wish to distinguish whether $p=q$ versus $p$ is at least $\eps$-far from $q$, in either $\ell_1$ or $\ell_2$ distance. Batu et al. gave the first sub-linear time algorithms for these problems, which matched the lower bounds of Valiant up to a logarithmic factor in $n$, and a polynomial factor of $\eps.$ #R##N#In this work, we present simple (and new) testers for both the $\ell_1$ and $\ell_2$ settings, with sample complexity that is information-theoretically optimal, to constant factors, both in the dependence on $n$, and the dependence on $\eps$; for the $\ell_1$ testing problem we establish that the sample complexity is $\Theta(\max\{n^{2/3}/\eps^{4/3}, n^{1/2}/\eps^2 \}).$

Connected through hyperedge HE-1 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples"):
  with members:
    - "towards testing monotonicity of distributions over general posets"
    - "fast and sample near optimal algorithms for learning multidimensional histograms"
    - "on learning mixtures of well separated gaussians"
    - "efficient robust proper learning of log concave distributions"
    - "collision based testers are optimal for uniformity and closeness"
    - "testing bayesian networks"
    - "on the structure covering and learning of poisson multinomial distributions"
    - "optimal testing for properties of distributions"

Connected through hyperedge HE-2 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning"):
  with members:
    - "efficiently learning and sampling interventional distributions from observations"
    - "learning and testing causal models with interventions"
    - "learning graphical models using multiplicative weights"
    - "testing ising models"
    - "square hellinger subadditivity for bayesian networks and its applications to identity testing"
    - "testing bayesian networks"
    - "causal networks semantics and expressiveness"
    - "testing probability distributions using conditional samples"

Connected through hyperedge HE-3 (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-4 (induced by source paper "communication and memory efficient testing of discrete distributions"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "two party distribution testing communication and security"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "near optimal closeness testing of discrete histogram distributions"
    - "sample optimal identity testing with high probability"
    - "sharp bounds for generalized uniformity testing"
    - "generalized uniformity testing"
    - "testing conditional independence of discrete distributions"

Connected through hyperedge HE-5 (induced by source paper "inference under information constraints ii communication constraints and shared randomness"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "test without trust optimal locally private distribution testing"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "sample optimal identity testing with high probability"
    - "a new approach for testing properties of discrete distributions"
    - "the optimality of correlated sampling"
    - "information theoretic lower bounds on bayes risk in decentralized estimation"

Connected through hyperedge HE-6 (induced by source paper "pan private uniformity testing"):
  with members:
    - "exponential separations in local differential privacy"
    - "communication and memory efficient testing of discrete distributions"
    - "the role of interactivity in local differential privacy"
    - "test without trust optimal locally private distribution testing"
    - "usable differential privacy a case study with psi"
    - "distributed differential privacy via shuffling"
    - "differentially private testing of identity and closeness of discrete distributions"
    - "heavy hitters and the structure of local privacy"

Connected through hyperedge HE-7 (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"
    - "testing closeness with unequal sized samples"
    - "testing closeness of discrete distributions"

Connected through hyperedge HE-8 (induced by source paper "testing properties of multiple distributions with few samples"):
  with members:
    - "generalized uniformity testing"
    - "learning populations of parameters"
    - "a new approach for testing properties of discrete distributions"
    - "collision based testers are optimal for uniformity and closeness"
    - "testing identity of structured distributions"
    - "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.DS(Data Structures and Algorithms)