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: "k means for streaming and distributed big sparse data"
  Abstract: We provide the first streaming algorithm for computing a provable approximation to the $k$-means of sparse Big data. Here, sparse Big Data is a set of $n$ vectors in $\mathbb{R}^d$, where each vector has $O(1)$ non-zeroes entries, and $d\geq n$. E.g., adjacency matrix of a graph, web-links, social network, document-terms, or image-features matrices. #R##N#Our streaming algorithm stores at most $\log n\cdot k^{O(1)}$ input points in memory. If the stream is distributed among $M$ machines, the running time reduces by a factor of $M$, while communicating a total of $M\cdot k^{O(1)}$ (sparse) input points between the machines. #R##N#% Our main technical result is a deterministic algorithm for computing a sparse $(k,\epsilon)$-coreset, which is a weighted subset of $k^{O(1)}$ input points that approximates the sum of squared distances from the $n$ input points to every $k$ centers, up to $(1\pm\epsilon)$ factor, for any given constant $\epsilon>0$. This is the first such coreset of size independent of both $d$ and $n$. #R##N#Existing algorithms use coresets of size at least polynomial in $d$, or project the input points on a subspace which diminishes their sparsity, thus require memory and communication $\Omega(d)=\Omega(n)$ even for $k=2$. #R##N#Experimental results real public datasets shows that our algorithm boost the performance of such given heuristics even in the off-line setting. Open code is provided for reproducibility.

Connected through hyperedge HE-1 (induced by source paper "sets clustering"):
  with members:
    - "introduction to coresets accurate coresets"
    - "tight sensitivity bounds for smaller coresets"
    - "k means clustering of lines for big data"
    - "on coresets for logistic regression"
    - "coresets and sketches"
    - "new frameworks for offline and streaming coreset constructions"
    - "improved and simplified inapproximability for k means"
    - "local search based approximation algorithms for mobile facility location problems"

Connected through hyperedge HE-2 (induced by source paper "scalable distributed approximation of internal measures for clustering evaluation"):
  with members:
    - "accurate mapreduce algorithms for k median and k means in general metric spaces"
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "clustering uncertain graphs"
    - "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"
    - "communication steps for parallel query processing"
    - "space round tradeoffs for mapreduce computations"

Connected through hyperedge HE-3 (induced by source paper "coresets for gaussian mixture models of any shape"):
  with members:
    - "map inference via block coordinate frank wolfe algorithm"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "minimizing sum of non convex but piecewise log lipschitz functions using coresets"
    - "learning mixture of gaussians with streaming data"
    - "on the k means median cost function"
    - "secure search on the cloud via coresets and sketches"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "new frameworks for offline and streaming coreset constructions"

Connected through hyperedge HE-4 (induced by source paper "robust coreset construction for distributed machine learning"):
  with members:
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "federated learning strategies for improving communication efficiency"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "federated multi task learning"
    - "coresets and sketches"
    - "new frameworks for offline and streaming coreset constructions"
    - "communication efficient learning of deep networks from decentralized data"
    - "geometric median in nearly linear time"

Connected through hyperedge HE-5 (induced by source paper "tight sensitivity bounds for smaller coresets"):
  with members:
    - "fast and accurate least mean squares solvers"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "new frameworks for offline and streaming coreset constructions"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "on the sensitivity of shape fitting problems"

Connected through hyperedge HE-6 (induced by source paper "aligning points to lines provable approximations"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "new frameworks for offline and streaming coreset constructions"
    - "mlpnp a real time maximum likelihood solution to the perspective n point problem"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-7 (induced by source paper "on coresets for logistic regression"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "recursive sampling for the nystr om method"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "input sparsity and hardness for robust subspace approximation"
    - "sketching as a tool for numerical linear algebra"
    - "uniform sampling for matrix approximation"
    - "subspace embeddings and ell_p regression using exponential random variables"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-8 (induced by source paper "differentially private k means with constant multiplicative error"):
  with members:
    - "practical locally private heavy hitters"
    - "clustering small samples with quality guarantees adaptivity with one2all pps"
    - "heavy hitters and the structure of local privacy"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "simultaneous private learning of multiple concepts"
    - "k means has polynomial smoothed complexity"

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)