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 "big data on the rise testing monotonicity of distributions"):
  Abstract: The field of property testing of probability distributions, or distribution testing, aims to provide fast and (most likely) correct answers to questions pertaining to specific aspects of very large datasets. In this work, we consider a property of particular interest, monotonicity of distributions. We focus on the complexity of monotonicity testing across different models of access to the distributions; and obtain results in these new settings that differ significantly from the known bounds in the standard sampling model.

Includes members:
  Member "testing identity of structured distributions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "testing probability distributions using conditional samples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")
  Member "on the power of conditional samples in distribution testing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")
  Member "learning k modal distributions via testing":
    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 "testing k modal distributions optimal algorithms via reductions":
    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 "streaming and sublinear approximation of entropy and information distances":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "uncertainty about uncertainty near optimal adaptive algorithms for estimating binary mixtures of unknown coins")

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)