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 "distributed simulation and distributed inference"):
  Abstract: Independent samples from an unknown probability distribution $\mathbf{p}$ on a domain of size $k$ are distributed across $n$ players, with each player holding one sample. Each player can communicate $\ell$ bits to a central referee in a simultaneous message passing (SMP) model of communication to help the referee infer a property of the unknown $\mathbf{p}$. When $\ell\geq\log k$ bits, the problem reduces to the well-studied collocated case where all the samples are available in one place. In this work, we focus on the communication-starved setting of $\ell < \log k$, in which the landscape may change drastically. We propose a general formulation for inference problems in this distributed setting, and instantiate it to two prototypical inference questions: learning and uniformity testing.

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 and memory efficient testing of discrete distributions")
  Member "test without trust optimal locally private distribution testing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locally private hypothesis selection")
  Member "sample optimal identity testing with high probability":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "communication and memory efficient testing of discrete distributions")
  Member "the optimality of correlated sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication for generating correlation a unifying survey")
  Member "information theoretic lower bounds on bayes risk in decentralized estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ratq a universal fixed length quantizer for stochastic optimization")
  Member "neyman pearson test for zero rate multiterminal hypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed hypothesis testing cooperation and concurrent detection")
  Member "communication lower bounds for statistical estimation problems via a distributed data processing inequality":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "locally private hypothesis selection")
  Member "testing shape restrictions of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")

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)