human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "communication optimal distributed clustering"
  Abstract: Clustering large datasets is a fundamental problem with a number of applications in machine learning. Data is often collected on different sites and clustering needs to be performed in a distributed manner with low communication. We would like the quality of the clustering in the distributed setting to match that in the centralized setting for which all the data resides on a single site. In this work, we study both graph and geometric clustering problems in two distributed models: (1) a point-to-point model, and (2) a model with a broadcast channel. We give protocols in both models which we show are nearly optimal by proving almost matching communication lower bounds. Our work highlights the surprising power of a broadcast channel for clustering problems; roughly speaking, to spectrally cluster $n$ points or $n$ vertices in a graph distributed across $s$ servers, for a worst-case partitioning the communication complexity in a point-to-point model is $n \cdot s$, while in the broadcast model it is $n + s$. A similar phenomenon holds for the geometric setting as well. We implement our algorithms and demonstrate this phenomenon on real life datasets, showing that our algorithms are also very efficient in practice.

Connected through hyperedge HE-1 (induced by source paper "the communication complexity of optimization"):
  with members:
    - "distributed and streaming linear programming in low dimensions"
    - "solving linear programs in the current matrix multiplication time"
    - "leveraging well conditioned bases streaming distributed summaries in minkowski p norms"
    - "distributed statistical estimation of matrix products with applications"
    - "l1 regression using lewis weights preconditioning and stochastic gradient descent"
    - "on communication complexity of classification problems"
    - "distributed low rank approximation of implicit functions of a matrix"
    - "communication efficient distributed kernel principal component analysis"

Connected through hyperedge HE-2 (induced by source paper "towards optimal moment estimation in streaming and distributed models"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "weighted reservoir sampling from distributed streams"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "distributed statistical estimation of matrix products with applications"
    - "revisiting frequency moment estimation in random order streams"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "continuous monitoring of ell_p norms in data streams"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"

Connected through hyperedge HE-3 (induced by source paper "a practical algorithm for distributed clustering and outlier detection"):
  with members:
    - "distributed partial clustering"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "improved distributed principal component analysis"
    - "optimal time bounds for approximate clustering"
    - "scalable k means"
    - "fast clustering using mapreduce"

Connected through hyperedge HE-4 (induced by source paper "distributed k clustering for data with heavy noise"):
  with members:
    - "distributed partial clustering"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "fast clustering using mapreduce"

Connected through hyperedge HE-5 (induced by source paper "communication optimal distributed dynamic graph clustering"):
  with members:
    - "online row sampling"
    - "partitioning well clustered graphs spectral clustering works"
    - "iterative row sampling"
    - "lower bounds for number in hand multiparty communication complexity made easy"

Connected through hyperedge HE-6 (induced by source paper "robust communication optimal distributed clustering algorithms"):
  with members:
    - "perturbation resilient clustering for k center and related problems via lp relaxations"
    - "distributed partial clustering"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "approximation algorithms for clustering problems with lower bounds and outliers"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "data driven resource allocation for distributed learning"
    - "k center clustering under perturbation resilience"
    - "a bi criteria approximation algorithm for k means"

Connected through hyperedge HE-7 (induced by source paper "distributed graph clustering and sparsification"):
  with members:
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "average whenever you meet opportunistic protocols for community detection"
    - "find your place simple distributed algorithms for community detection"
    - "constructing linear sized spectral sparsification in almost linear time"
    - "partitioning well clustered graphs spectral clustering works"
    - "partitioning into expanders"
    - "approximating the expansion profile and almost optimal local graph clustering"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"

Connected through hyperedge HE-8 (induced by source paper "learning graphical models from a distributed stream"):
  with members:
    - "benchmarking distributed stream processing platforms for iot applications"
    - "graphical model sketch"
    - "tensorflow a system for large scale machine learning"
    - "mllib machine learning in apache spark"
    - "distributed learning communication complexity and privacy"
    - "randomized algorithms for tracking distributed count frequencies and ranks"
    - "lower bounds for number in hand multiparty communication complexity made easy"

Question: Please tell me which class the center node 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)