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 "monotone probability distributions over the boolean cube can be learned with sublinear samples"):
  Abstract: A probability distribution over the Boolean cube is monotone if flipping the value of a coordinate from zero to one can only increase the probability of an element. Given samples of an unknown monotone distribution over the Boolean cube, we give (to our knowledge) the first algorithm that learns an approximation of the distribution in statistical distance using a number of samples that is sublinear in the domain. #R##N#To do this, we develop a structural lemma describing monotone probability distributions. The structural lemma has further implications to the sample complexity of basic testing tasks for analyzing monotone probability distributions over the Boolean cube: We use it to give nontrivial upper bounds on the tasks of estimating the distance of a monotone distribution to uniform and of estimating the support size of a monotone distribution. In the setting of monotone probability distributions over the Boolean cube, our algorithms are the first to have sample complexity lower than known lower bounds for the same testing tasks on arbitrary (not necessarily monotone) probability distributions. #R##N#One further consequence of our learning algorithm is an improved sample complexity for the task of testing whether a distribution on the Boolean cube is monotone.

Includes members:
  Member "towards testing monotonicity of distributions over general posets":
    also connected through other hyperedges:
      (none)
  Member "fast and sample near optimal algorithms for learning multidimensional histograms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a polynomial time algorithm for log concave maximum likelihood via locally exponential families")
  Member "on learning mixtures of well separated gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "efficient robust proper learning of log concave distributions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a polynomial time algorithm for log concave maximum likelihood via locally exponential families")
  Member "collision based testers are optimal for uniformity and closeness":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication and memory efficient testing of discrete distributions")
  Member "testing bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")
  Member "on the structure covering and learning of poisson multinomial distributions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "private hypothesis selection")
  Member "optimal testing for properties of distributions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "private hypothesis selection")

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)