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 "testing ising models"):
  Abstract: Given samples from an unknown multivariate distribution $p$, is it possible to distinguish whether $p$ is the product of its marginals versus $p$ being far from every product distribution? Similarly, is it possible to distinguish whether $p$ equals a given distribution $q$ versus $p$ and $q$ being far from each other? These problems of testing independence and goodness-of-fit have received enormous attention in statistics, information theory, and theoretical computer science, with sample-optimal algorithms known in several interesting regimes of parameters. Unfortunately, it has also been understood that these problems become intractable in large dimensions, necessitating exponential sample complexity. #R##N#Motivated by the exponential lower bounds for general distributions as well as the ubiquity of Markov Random Fields (MRFs) in the modeling of high-dimensional distributions, we initiate the study of distribution testing on structured multivariate distributions, and in particular the prototypical example of MRFs: the Ising Model. We demonstrate that, in this structured setting, we can avoid the curse of dimensionality, obtaining sample and time efficient testers for independence and goodness-of-fit. Along the way, we develop new tools for establishing concentration of functions of the Ising model, using the exchangeable pairs framework developed by Chatterjee, and improving upon this framework. In particular, we prove tighter concentration results for multi-linear functions of the Ising model in the high-temperature regime.

Includes members:
  Member "lower bounds for testing graphical models colorings and antiferromagnetic ising models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "privately learning markov random fields")
  Member "regression from dependent observations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how bad is worst case data if you know where it comes from")
  Member "learning from weakly dependent data under dobrushin s condition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mcdiarmid type inequalities for graph dependent variables and stability bounds")
  Member "private identity testing for high dimensional distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "locally private hypothesis selection")
  Member "learning and testing causal models with interventions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")
  Member "sparse logistic regression learns all discrete pairwise graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "privately learning markov random fields")
  Member "testing identity of multidimensional histograms":
    also connected through other hyperedges:
      (none)
  Member "near optimal closeness testing of discrete histogram distributions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "communication and memory efficient testing of discrete distributions")

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.DS(Data Structures and Algorithms)