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 of discrete distributions"
  Abstract: Given samples from two distributions over an $n$-element set, we wish to test whether these distributions are statistically close. We present an algorithm which uses sublinear in $n$, specifically, $O(n^{2/3}\epsilon^{-8/3}\log n)$, independent samples from each distribution, runs in time linear in the sample size, makes no assumptions about the structure of the distributions, and distinguishes the cases when the distance between the distributions is small (less than $\max\{\epsilon^{4/3}n^{-1/3}/32, \epsilon n^{-1/2}/4\}$) or large (more than $\epsilon$) in $\ell_1$ distance. This result can be compared to the lower bound of $\Omega(n^{2/3}\epsilon^{-2/3})$ for this problem given by Valiant. #R##N#Our algorithm has applications to the problem of testing whether a given Markov process is rapidly mixing. We present sublinear for several variants of this problem as well.

Connected through hyperedge HE-1 (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"
    - "optimal algorithms for testing closeness of discrete distributions"

Connected through hyperedge HE-2 (induced by source paper "domain compression and its application to randomness optimal distributed goodness of fit"):
  with members:
    - "inference under information constraints ii communication constraints and shared randomness"
    - "communication complexity in locally private distribution estimation and heavy hitters"
    - "communication and memory efficient testing of discrete distributions"
    - "private identity testing for high dimensional distributions"
    - "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"
    - "locally private hypothesis testing"

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"
    - "testing closeness with unequal sized samples"
    - "optimal algorithms for testing closeness of discrete distributions"

Connected through hyperedge HE-4 (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"
    - "optimal algorithms for testing closeness of discrete distributions"

Connected through hyperedge HE-5 (induced by source paper "property testing for differential privacy"):
  with members:
    - "detecting violations of differential privacy"
    - "renyi differential privacy"
    - "collecting telemetry data privately"
    - "which distribution distances are sublinearly testable"
    - "concentrated differential privacy"
    - "optimal testing for properties of distributions"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "differential privacy and machine learning a survey and review"

Connected through hyperedge HE-6 (induced by source paper "sampling correctors"):
  with members:
    - "sample optimal density estimation in nearly linear time"
    - "testing probability distributions underlying aggregated data"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"
    - "efficient density estimation via piecewise polynomial approximation"
    - "learning mixtures of structured distributions over discrete domains"
    - "local reconstructors and tolerant testers for connectivity and diameter"
    - "on the power of conditional samples in distribution testing"
    - "learning poisson binomial distributions"

Connected through hyperedge HE-7 (induced by source paper "second order asymptotically optimal statistical classification"):
  with members:
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "asymptotically optimal matching of multiple sequences to source distributions and training sequences"
    - "second order coding rates for channels with state"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "information spectrum approach to second order coding rate in channel coding"

Connected through hyperedge HE-8 (induced by source paper "two party distribution testing communication and security"):
  with members:
    - "optimal testing for properties of 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)