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 shape restrictions of discrete distributions"):
  Abstract: We study the question of testing structured properties (classes) of discrete distributions. Specifically, given sample access to an arbitrary distribution $D$ over $[n]$ and a property $\mathcal{P}$, the goal is to distinguish between $D\in\mathcal{P}$ and $\ell_1(D,\mathcal{P})>\varepsilon$. We develop a general algorithm for this question, which applies to a large range of "shape-constrained" properties, including monotone, log-concave, $t$-modal, piecewise-polynomial, and Poisson Binomial distributions. Moreover, for all cases considered, our algorithm has near-optimal sample complexity with regard to the domain size and is computationally efficient. For most of these classes, we provide the first non-trivial tester in the literature. In addition, we also describe a generic method to prove lower bounds for this problem, and use it to show our upper bounds are nearly tight. Finally, we extend some of our techniques to tolerant testing, deriving nearly-tight upper and lower bounds for the corresponding questions.

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 "improving and extending the testing of distributions for shape restricted properties":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "anaconda a non adaptive conditional sampling algorithm for distribution testing")
  Member "efficient robust proper learning of log concave distributions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "optimal testing for properties of distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "optimal learning via the fourier transform for sums of independent integer random variables":
    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 "sample optimal density estimation in nearly linear time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning structured distributions from untrusted batches faster and simpler")
  Member "optimal algorithms and lower bounds for testing closeness of structured distributions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "communication and memory efficient testing of discrete distributions")
  Member "testing poisson binomial distributions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning sums of independent random variables with sparse collective support")

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)