human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "property testing of joint distributions using conditional samples"):
  Abstract: In this paper, we present the first non-trivial property tester for joint probability distributions in the recently introduced conditional sampling model. The conditional sampling framework provides an oracle for a distribution $\mu$ that takes as input a subset $S$ of the domain $\Omega$ and returns a sample from the distribution $\mu$ conditioned on $S$.For a joint distribution of dimension $n$, we give a $\tilde{\mathcal{O}}(n^3)$-query uniformity tester, a $\tilde{\mathcal{O}}(n^3)$-query identity tester with a known distribution, and a $\tilde{\mathcal{O}}(n^6)$-query tester for testing independence of marginals. Our technique involves an elegant chain rule which can be proved using basic techniques of probability theory, yet powerful enough to avoid the curse of dimensionality. #R##N#We also prove a sample complexity lower bound of $\Omega(\sqrt[4]{n})$ for testing uniformity of a joint distribution when the tester is only allowed to condition independently on the marginals. Our technique involves novel relations between Hellinger distance and total variational distance, and may be of independent interest.

Includes members:
  Member "a new approach for testing properties of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "private identity testing for high dimensional distributions")
  Member "testing ising models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "privately learning markov random fields")
  Member "square hellinger subadditivity for bayesian networks and its applications to identity testing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "subadditivity of probability divergences on bayes nets with applications to time series gans")
  Member "faster sublinear algorithms using conditional sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "anaconda a non adaptive conditional sampling algorithm for distribution testing")
  Member "testing bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "faster algorithms for testing under conditional sampling":
    also connected through other hyperedges:
      (none)
  Member "optimal testing for properties of distributions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "big data on the rise testing monotonicity of distributions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards testing monotonicity of distributions over general posets")

Question: Please tell me which class the source paper 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.CC(Computational Complexity)