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 lower bounds for statistical estimation problems via a distributed data processing inequality"):
  Abstract: We study the tradeoff between the statistical error and communication cost of distributed statistical estimation problems in high dimensions. In the distributed sparse Gaussian mean estimation problem, each of the $m$ machines receives $n$ data points from a $d$-dimensional Gaussian distribution with unknown mean $\theta$ which is promised to be $k$-sparse. The machines communicate by message passing and aim to estimate the mean $\theta$. We provide a tight (up to logarithmic factors) tradeoff between the estimation error and the number of bits communicated between the machines. This directly leads to a lower bound for the distributed \textit{sparse linear regression} problem: to achieve the statistical minimax error, the total communication is at least $\Omega(\min\{n,d\}m)$, where $n$ is the number of observations that each machine receives and $d$ is the ambient dimension. These lower results improve upon [Sha14,SD'14] by allowing multi-round iterative communication model. We also give the first optimal simultaneous protocol in the dense case for mean estimation. #R##N#As our main technique, we prove a \textit{distributed data processing inequality}, as a generalization of usual data processing inequalities, which might be of independent interest and useful for other problems.

Includes members:
  Member "distributed low rank approximation of implicit functions of a matrix":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the communication complexity of optimization")
  Member "optimal principal component analysis in distributed and streaming models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "on communication cost of distributed statistical estimation and dimensionality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the communication complexity of optimization")
  Member "improved distributed principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the communication complexity of optimization")
  Member "strong data processing inequalities and phi sobolev inequalities for discrete channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on data processing and majorization inequalities for f divergences with applications")
  Member "fundamental limits of online and distributed algorithms for statistical learning and estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg")
  Member "communication efficient distributed optimization using an approximate newton type method":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "principal component analysis and higher correlations for distributed data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the communication complexity of optimization")

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)