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: "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"
  Abstract: The average distance from a node to all other nodes in a graph, or from a query point in a metric space to a set of points, is a fundamental quantity in data analysis. The inverse of the average distance, known as the (classic) closeness centrality of a node, is a popular importance measure in the study of social networks. We develop novel structural insights on the sparsifiability of the distance relation via weighted sampling. Based on that, we present highly practical algorithms with strong statistical guarantees for fundamental problems. We show that the average distance (and hence the centrality) for all nodes in a graph can be estimated using $O(\epsilon^{-2})$ single-source distance computations. For a set $V$ of $n$ points in a metric space, we show that after preprocessing which uses $O(n)$ distance computations we can compute a weighted sample $S\subset V$ of size $O(\epsilon^{-2})$ such that the average distance from any query point $v$ to $V$ can be estimated from the distances from $v$ to $S$. Finally, we show that for a set of points $V$ in a metric space, we can estimate the average pairwise distance using $O(n+\epsilon^{-2})$ distance computations. The estimate is based on a weighted sample of $O(\epsilon^{-2})$ pairs of points, which is computed using $O(n)$ distance computations. Our estimates are unbiased with normalized mean square error (NRMSE) of at most $\epsilon$. Increasing the sample size by a $O(\log n)$ factor ensures that the probability that the relative error exceeds $\epsilon$ is polynomially small.

Connected through hyperedge HE-1 (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"
    - "k means for streaming and distributed big sparse data"
    - "communication steps for parallel query processing"
    - "space round tradeoffs for mapreduce computations"

Connected through hyperedge HE-2 (induced by source paper "computing top k closeness centrality faster in unweighted graphs"):
  with members:
    - "a note on the complexity of computing the number of reachable vertices in a digraph"
    - "fast and simple computation of top k closeness centralities"
    - "approximation and fixed parameter subquadratic algorithms for radius and diameter"
    - "powers of tensors and fast matrix multiplication"
    - "networkit an interactive tool suite for high performance network analysis"
    - "networkit a tool suite for large scale complex network analysis"
    - "popular conjectures imply strong lower bounds for dynamic problems"
    - "into the square on the complexity of quadratic time solvable problems"

Connected through hyperedge HE-3 (induced by source paper "clustering small samples with quality guarantees adaptivity with one2all pps"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a constant factor bi criteria approximation guarantee for k means"
    - "multi objective weighted sampling"
    - "the hardness of approximation of euclidean k means"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "optimal time bounds for approximate clustering"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-4 (induced by source paper "scaling up group closeness maximization"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "engineering parallel algorithms for community detection in massive networks"
    - "scalable betweenness centrality maximization via sampling"
    - "fast and simple computation of top k closeness centralities"
    - "networkit a tool suite for large scale complex network analysis"
    - "computing classic closeness centrality at scale"
    - "fast approximation of centrality"

Connected through hyperedge HE-5 (induced by source paper "metric 1 median selection query complexity vs approximation ratio"):
  with members:
    - "metric 1 median selection with fewer queries"
    - "a deterministic sublinear time nonadaptive algorithm for metric 1 median selection"
    - "a lower bound for metric 1 median selection"
    - "a simple d 2 sampling based ptas for k means and other clustering problems"
    - "optimal time bounds for approximate clustering"
    - "fast approximation of centrality"

Connected through hyperedge HE-6 (induced by source paper "multi objective weighted sampling"):
  with members:
    - "stream sampling for frequency cap statistics"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "hyperanf approximating the neighbourhood function of very large graphs on a budget"
    - "coordinated weighted sampling for estimating aggregates over multiple weight assignments"
    - "stream sampling for variance optimal estimation of subset sums"

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.SI(Social and Information Networks)