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: "scalable betweenness centrality maximization via sampling"
  Abstract: Betweenness centrality is a fundamental centrality measure in social network analysis. Given a large-scale network, how can we find the most central nodes? This question is of key importance to numerous important applications that rely on betweenness centrality, including community detection and understanding graph vulnerability. Despite the large amount of work on designing scalable approximation algorithms for betweenness centrality, estimating it on large-scale networks remains a computational challenge. #R##N#In this paper, we study the Betweenness Centrality Maximization problem: given a graph $G=(V,E)$ and a positive integer $k$, find a set $S^* \subseteq V$ that maximizes betweenness centrality subject to the cardinality constraint $|S^*| \leq k$. We present an efficient randomized algorithm that provides a $(1-1/e-\epsilon)$-approximation with high probability, where $\epsilon>0$. Our results improve the current state-of-the-art result by Yoshida~\cite{yoshida2014almost}. Furthermore, we provide theoretical evidence for the validity of a crucial assumption in the literature of betweenness centrality estimation, namely that in real-world networks $O(|V|^2)$ shortest paths pass through the top-$k$ central nodes, where $k$ is a constant. On the experimental side, we perform an extensive experimental analysis of our method on real-world networks, demonstrate its accuracy and scalability, and study different properties of central nodes. Finally, we provide three graph mining applications of our method.

Connected through hyperedge HE-1 (induced by source paper "learning to identify high betweenness centrality nodes from scratch a novel graph neural network approach"):
  with members:
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "enhanced network embeddings via exploiting edge labels"
    - "graph edit distance computation via graph neural networks"
    - "learning structural node embeddings via diffusion wavelets"
    - "representation learning on graphs methods and applications"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-2 (induced by source paper "current flow group closeness centrality for complex networks"):
  with members:
    - "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms"
    - "scaling up group closeness maximization"
    - "estimating current flow closeness centrality with a multigrid laplacian solver"
    - "measuring and maximizing influence via random walk in social activity networks"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "absorbing random walk centrality theory and algorithms"
    - "axioms for centrality"
    - "approximate maximum flow on separable undirected graphs"

Connected through hyperedge HE-3 (induced by source paper "group centrality maximization for large scale graphs"):
  with members:
    - "network density of states"
    - "current flow group closeness centrality for complex networks"
    - "pitfalls of graph neural network evaluation"
    - "scalable katz ranking computation in large static and dynamic graphs"
    - "computing top k closeness centrality faster in unweighted graphs"
    - "scaling up group closeness maximization"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "approximate k cover in hypergraphs efficient algorithms and applications"):
  with members:
    - "outward influence and cascade size estimation in billion scale networks"
    - "better streaming algorithms for the maximum coverage problem"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "scalable influence estimation in continuous time diffusion networks"
    - "maximizing social influence in nearly optimal time"
    - "irie scalable and robust influence maximization in social networks"

Connected through hyperedge HE-5 (induced by source paper "covert networks how hard is it to hide"):
  with members:
    - "hiding individuals and communities in a social network"
    - "eth hardness for densest k subgraph with perfect completeness"

Connected through hyperedge HE-6 (induced by source paper "maximizing the number of spanning trees in a connected graph"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "a nearly linear time algorithm for submodular maximization with a knapsack constraint"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "subdeterminant maximization via nonconvex relaxations and anti concentration"
    - "determinant preserving sparsification of sddm matrices with applications to counting and sampling spanning trees"

Connected through hyperedge HE-7 (induced by source paper "novel adaptive algorithms for estimating betweenness coverage and k path centralities"):
  with members:
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"

Connected through hyperedge HE-8 (induced by source paper "improving the betweenness centrality of a node by adding links"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "faster betweenness centrality updates in evolving networks"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "approximating betweenness centrality in fully dynamic networks"
    - "approximation algorithms for reducing the spectral radius to control epidemic spread"
    - "networkit a tool suite for large scale complex network analysis"
    - "scalable online betweenness centrality in evolving graphs"

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)