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 "communication and memory efficient testing of discrete distributions"):
  Abstract: We study distribution testing with communication and memory constraints in the following computational models: (1) The {\em one-pass streaming model} where the goal is to minimize the sample complexity of the protocol subject to a memory constraint, and (2) A {\em distributed model} where the data samples reside at multiple machines and the goal is to minimize the communication cost of the protocol. In both these models, we provide efficient algorithms for uniformity/identity testing (goodness of fit) and closeness testing (two sample testing). Moreover, we show nearly-tight lower bounds on (1) the sample complexity of any one-pass streaming tester for uniformity, subject to the memory constraint, and (2) the communication cost of any uniformity testing protocol, in a restricted `one-pass' model of communication.

Includes members:
  Member "geometric lower bounds for distributed parameter estimation under communication constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "communication complexity in locally private distribution estimation and heavy hitters")
  Member "two party distribution testing communication and security":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inference under information constraints i lower bounds from chi square contraction")
  Member "inference under information constraints i lower bounds from chi square contraction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "locally private hypothesis selection")
  Member "near optimal closeness testing of discrete histogram distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "testing identity of multidimensional histograms")
  Member "sample optimal identity testing with high probability":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lower bounds for testing graphical models colorings and antiferromagnetic ising models")
  Member "sharp bounds for generalized uniformity testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "testing identity of multidimensional histograms")
  Member "generalized uniformity testing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "testing properties of multiple distributions with few samples")
  Member "testing conditional independence of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "private identity testing for high dimensional 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.LG(Machine Learning)