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 probability distributions using conditional samples"
  Abstract: We study a new framework for property testing of probability distributions, by considering distribution testing algorithms that have access to a conditional sampling oracle.* This is an oracle that takes as input a subset $S \subseteq [N]$ of the domain $[N]$ of the unknown probability distribution $D$ and returns a draw from the conditional probability distribution $D$ restricted to $S$. This new model allows considerable flexibility in the design of distribution testing algorithms; in particular, testing algorithms in this model can be adaptive. #R##N#We study a wide range of natural distribution testing problems in this new framework and some of its variants, giving both upper and lower bounds on query complexity. These problems include testing whether $D$ is the uniform distribution $\mathcal{U}$; testing whether $D = D^\ast$ for an explicitly provided $D^\ast$; testing whether two unknown distributions $D_1$ and $D_2$ are equivalent; and estimating the variation distance between $D$ and the uniform distribution. At a high level our main finding is that the new "conditional sampling" framework we consider is a powerful one: while all the problems mentioned above have $\Omega(\sqrt{N})$ sample complexity in the standard model (and in some cases the complexity must be almost linear in $N$), we give $\mathrm{poly}(\log N, 1/\varepsilon)$-query algorithms (and in some cases $\mathrm{poly}(1/\varepsilon)$-query algorithms independent of $N$) for all these problems in our conditional sampling setting. #R##N#*Independently from our work, Chakraborty et al. also considered this framework. We discuss their work in Subsection [1.4].

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 "uncertainty about uncertainty near optimal adaptive algorithms for estimating binary mixtures of unknown coins"):
  with members:
    - "maximum selection and ranking under noisy comparisons"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "learning populations of parameters"
    - "feeling the bern adaptive estimators for bernoulli probabilities of pairwise comparisons"
    - "a data quality metric dqm how to estimate the number of undetected errors in data sets"
    - "a polynomial lower bound for testing monotonicity"
    - "approval voting and incentives in crowdsourcing"
    - "simple robust and optimal ranking from pairwise comparisons"

Connected through hyperedge HE-3 (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-4 (induced by source paper "lower bounds for tolerant junta and unateness testing via rejection sampling of graphs"):
  with members:
    - "a lower bound for nonadaptive one sided error testing of unateness of boolean functions over the hypercube"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "settling the query complexity of non adaptive junta testing"
    - "optimal unateness testers for real valued functions adaptivity helps"
    - "a widetilde o n non adaptive tester for unateness"
    - "a polynomial lower bound for testing monotonicity"
    - "on the power of conditional samples in distribution testing"

Connected through hyperedge HE-5 (induced by source paper "property testing of joint distributions using conditional samples"):
  with members:
    - "a new approach for testing properties of discrete distributions"
    - "testing ising models"
    - "square hellinger subadditivity for bayesian networks and its applications to identity testing"
    - "faster sublinear algorithms using conditional sampling"
    - "testing bayesian networks"
    - "faster algorithms for testing under conditional sampling"
    - "optimal testing for properties of distributions"
    - "big data on the rise testing monotonicity of distributions"

Connected through hyperedge HE-6 (induced by source paper "adaptivity is exponentially powerful for testing monotonicity of halfspaces"):
  with members:
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "settling the query complexity of non adaptive junta testing"
    - "optimal unateness testers for real valued functions adaptivity helps"
    - "a polynomial lower bound for testing monotonicity"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "new algorithms and lower bounds for monotonicity testing"
    - "an optimal lower bound for monotonicity testing over hypergrids"
    - "a o n monotonicity tester for boolean functions over the hypercube"

Connected through hyperedge HE-7 (induced by source paper "improving and extending the testing of distributions for shape restricted properties"):
  with members:
    - "a new approach for testing properties of discrete distributions"
    - "optimal testing for properties of distributions"
    - "testing shape restrictions of discrete distributions"
    - "a chasm between identity and equivalence testing with conditional queries"
    - "on the power of conditional samples in distribution testing"

Connected through hyperedge HE-8 (induced by source paper "testing shape restrictions of discrete distributions"):
  with members:
    - "a new approach for testing properties of discrete distributions"
    - "improving and extending the testing of distributions for shape restricted properties"
    - "efficient robust proper learning of log concave distributions"
    - "optimal testing for properties of distributions"
    - "optimal learning via the fourier transform for sums of independent integer random variables"
    - "sample optimal density estimation in nearly linear time"
    - "optimal algorithms and lower bounds for testing closeness of structured distributions"
    - "testing poisson binomial 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)