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: "approximation algorithms for reducing the spectral radius to control epidemic spread"
  Abstract: The largest eigenvalue of the adjacency matrix of a network (referred to as the spectral radius) is an important metric in its own right. Further, for several models of epidemic spread on networks (e.g., the `flu-like' SIS model), it has been shown that an epidemic dies out quickly if the spectral radius of the graph is below a certain threshold that depends on the model parameters. This motivates a strategy to control epidemic spread by reducing the spectral radius of the underlying network. #R##N#In this paper, we develop a suite of provable approximation algorithms for reducing the spectral radius by removing the minimum cost set of edges (modeling quarantining) or nodes (modeling vaccinations), with different time and quality tradeoffs. Our main algorithm, \textsc{GreedyWalk}, is based on the idea of hitting closed walks of a given length, and gives an $O(\log^2{n})$-approximation, where $n$ denotes the number of nodes; it also performs much better in practice compared to all prior heuristics proposed for this problem. We further present a novel sparsification method to improve its running time. #R##N#In addition, we give a new primal-dual based algorithm with an even better approximation guarantee ($O(\log n)$), albeit with slower running time. We also give lower bounds on the worst-case performance of some of the popular heuristics. Finally we demonstrate the applicability of our algorithms and the properties of our solutions via extensive experiments on multiple synthetic and real networks.

Connected through hyperedge HE-1 (induced by source paper "transient dynamics of epidemic spreading and its mitigation on large networks"):
  with members:
    - "on the dynamics of deterministic epidemic propagation over networks"
    - "campaigning in heterogeneous social networks optimal control of si information epidemics"
    - "analysis of exact and approximated epidemic models over complex networks"
    - "optimal patching in clustered malware epidemics"
    - "optimal energy aware epidemic routing in dtns"
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"

Connected through hyperedge HE-2 (induced by source paper "teals time aware text embedding approach to leverage subgraphs"):
  with members:
    - "improving topic models with latent feature word representations"
    - "learning word relatedness over time"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "algorithmic social intervention"):
  with members:
    - "robust optimization for non convex objectives"
    - "equilibrium computation and robust optimization in zero sum games with submodular structure"
    - "robust budget allocation via continuous submodular functions"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "submodular functions from discrete to continous domains"
    - "spectral redemption in clustering sparse networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-4 (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"
    - "scalable betweenness centrality maximization via sampling"
    - "approximating betweenness centrality in fully dynamic networks"
    - "networkit a tool suite for large scale complex network analysis"
    - "scalable online betweenness centrality in evolving graphs"

Connected through hyperedge HE-5 (induced by source paper "disabling external influence in social networks via edge recommendation"):
  with members:
    - "modification of social dominance in social networks by selective adjustment of interpersonal weights"
    - "reducing controversy by connecting opposing views"
    - "updating and downdating techniques for optimizing network communicability"
    - "incremental algorithms for network management and analysis based on closeness centrality"

Connected through hyperedge HE-6 (induced by source paper "towards scalable network delay minimization"):
  with members:
    - "the social media genome modeling individual topic specific behavior in social media"

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)