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: "modeling complex systems with adaptive networks"
  Abstract: Abstract   Adaptive networks are a novel class of dynamical networks whose topologies and states coevolve. Many real-world complex systems can be modeled as adaptive networks, including social networks, transportation networks, neural networks and biological networks. In this paper, we introduce fundamental concepts and unique properties of adaptive networks through a brief, non-comprehensive review of recent literature on mathematical/computational modeling and analysis of such networks. We also report our recent work on several applications of computational adaptive network modeling and analysis to real-world problems, including temporal development of search and rescue operational networks, automated rule discovery from empirical network evolution data, and cultural integration in corporate merger.

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 "detecting sequences of system states in temporal networks"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "concept drift and anomaly detection in graph streams"
    - "fast change point detection on dynamic social networks"
    - "measuring large scale social networks with high resolution"
    - "detecting change points in the large scale structure of evolving networks"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-3 (induced by source paper "beyond social fragmentation coexistence of cultural diversity and structural connectivity is possible with social constituent diversity"):
  with members:
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "optimal containment of epidemics over temporal activity driven networks"):
  with members:
    - "optimal containment of epidemics in temporal and adaptive networks"
    - "epidemic processes over adaptive state dependent 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:
    - "a framework for the construction of generative models for mesoscale structure in multilayer networks"
    - "generative benchmark models for mesoscale structures in multilayer networks"
    - "do cascades recur"
    - "pagerank beyond the web"
    - "multi stage complex contagions"

Connected through hyperedge HE-6 (induced by source paper "the role of network analysis in industrial and applied mathematics"):
  with members:
    - "generative benchmark models for mesoscale structures in multilayer networks"
    - "do cascades recur"
    - "multi stage complex contagions"

Connected through hyperedge HE-7 (induced by source paper "discordant voting processes on finite graphs"):
  with members:
    - "bounds on the voter model in dynamic networks"
    - "coalescing random walks and voting on connected 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)