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: "a framework for the construction of generative models for mesoscale structure in multilayer networks"
  Abstract: Multilayer networks allow one to represent diverse and coupled connectivity patterns --- e.g., time-dependence, multiple subsystems, or both --- that arise in many applications and which are difficult or awkward to incorporate into standard network representations. In the study of multilayer networks, it is important to investigate mesoscale (i.e., intermediate-scale) structures, such as dense sets of nodes known as communities, to discover network features that are not apparent at the microscale or the macroscale. The ill-defined nature of mesoscale structure and its ubiquity in empirical networks make it crucial to develop generative models that can produce the features that one encounters in empirical networks. Key purposes of such generative models include generating synthetic networks with empirical properties of interest, benchmarking mesoscale-detection methods and algorithms, and inferring structure in empirical multilayer networks. In this paper, we introduce a framework for the construction of generative models for mesoscale structures in multilayer networks. Our framework provides a standardized set of generative models, together with an associated set of principles from which they are derived, for studies of mesoscale structures in multilayer networks. It unifies and generalizes many existing models for mesoscale structures in fully-ordered (e.g., temporal) and unordered (e.g., multiplex) multilayer networks. One can also use it to construct generative models for mesoscale structures in partially-ordered multilayer networks (e.g., networks that are both temporal and multiplex). Our framework has the ability to produce many features of empirical multilayer networks, and it explicitly incorporates a user-specified dependency structure between layers.

Connected through hyperedge HE-1 (induced by source paper "nonlinearity networks a 2020 vision"):
  with members:
    - "models of continuous time networks with tie decay diffusion and convection"
    - "tunable eigenvector based centralities for multiplex and temporal networks"
    - "architecture and evolution of semantic networks in mathematics texts"
    - "supracentrality analysis of temporal networks with directed interlayer coupling"
    - "using persistent homology to quantify a diurnal cycle in hurricane felix"
    - "persistent homology of geospatial data a case study with voting"
    - "network structure from rich but noisy data"
    - "random walks on simplicial complexes and the normalized hodge 1 laplacian"

Connected through hyperedge HE-2 (induced by source paper "role detection in bicycle sharing networks using multilayer stochastic block models"):
  with members:
    - "relating modularity maximization and stochastic block models in multilayer networks"
    - "community discovery in dynamic networks a survey"
    - "a multi scale analysis of 27 000 urban street networks every us city town urbanized area and zillow neighborhood"
    - "random graph models for dynamic networks"
    - "the ground truth about metadata and community detection in networks"
    - "clustering network layers with the strata multilayer stochastic block model"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "generative benchmark models for mesoscale structures in multilayer networks"

Connected through hyperedge HE-3 (induced by source paper "challenges in community discovery on temporal networks"):
  with members:
    - "olcpm an online framework for detecting overlapping communities in dynamic social networks"
    - "community discovery in dynamic networks a survey"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "the ground truth about metadata and community detection in networks"
    - "snap a general purpose network analysis and graph mining library"
    - "generative benchmark models for mesoscale structures in multilayer networks"
    - "computing maximal cliques in link streams"

Connected through hyperedge HE-4 (induced by source paper "community discovery in dynamic networks a survey"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "revealing evolutions in dynamical networks"
    - "the ground truth about metadata and community detection in networks"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "generative benchmark models for mesoscale structures in multilayer networks"
    - "enumerating maximal cliques in temporal graphs"
    - "using multiple criteria methods to evaluate community partitions"
    - "predicting community evolution in social networks"

Connected through hyperedge HE-5 (induced by source paper "the role of network analysis in industrial and applied mathematics a physical applied mathematics perspective"):
  with members:
    - "generative benchmark models for mesoscale structures in multilayer networks"
    - "do cascades recur"
    - "pagerank beyond the web"
    - "multi stage complex contagions"
    - "modeling complex systems with adaptive networks"

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)