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: "multi layer graph analysis for dynamic social networks"
  Abstract: Modern social networks frequently encompass multiple distinct types of connectivity information; for instance, explicitly acknowledged friend relationships might complement behavioral measures that link users according to their actions or interests. One way to represent these networks is as multi-layer graphs, where each layer contains a unique set of edges over the same underlying vertices (users). Edges in different layers typically have related but distinct semantics; depending on the application multiple layers might be used to reduce noise through averaging, to perform multifaceted analyses, or a combination of the two. However, it is not obvious how to extend standard graph analysis techniques to the multi-layer setting in a flexible way. In this paper we develop latent variable models and methods for mining multi-layer networks for connectivity patterns based on noisy data.

Connected through hyperedge HE-1 (induced by source paper "latent heterogeneous multilayer community detection"):
  with members:
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "community extraction in multilayer networks with heterogeneous community structure"
    - "information extraction from larger multi layer social networks"

Connected through hyperedge HE-2 (induced by source paper "information diffusion in interconnected heterogeneous networks"):
  with members:
    - "information diffusion of topic propagation in social media"
    - "structure and dynamics of information pathways in online media"

Connected through hyperedge HE-3 (induced by source paper "phase transitions and a model order selection criterion for spectral graph clustering"):
  with members:
    - "estimating the number of communities in a network"
    - "spectral detection in the censored block model"
    - "universal phase transition in community detectability under a stochastic block model"
    - "local set based graph signal reconstruction"
    - "incremental method for spectral clustering of increasing orders"
    - "signal recovery on graphs variation minimization"
    - "exact recovery in the stochastic block model"
    - "phase transitions in spectral community detection"

Connected through hyperedge HE-4 (induced by source paper "multilayer spectral graph clustering via convex layer aggregation"):
  with members:
    - "phase transitions and a model order selection criterion for spectral graph clustering"
    - "clustering network layers with the strata multilayer stochastic block model"
    - "information extraction from larger multi layer social networks"
    - "principal patterns on graphs discovering coherent structures in datasets"
    - "universal phase transition in community detectability under a stochastic block model"
    - "multi centrality graph spectral decompositions and their application to cyber intrusion detection"
    - "deep community detection"
    - "dynamic stochastic blockmodels for time evolving social networks"

Connected through hyperedge HE-5 (induced by source paper "amos an automated model order selection algorithm for spectral graph clustering"):
  with members:
    - "phase transitions and a model order selection criterion for spectral graph clustering"
    - "spectral detection in the censored block model"
    - "local set based graph signal reconstruction"
    - "signal recovery on graphs variation minimization"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "phase transitions in spectral community detection"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-6 (induced by source paper "information diffusion of topic propagation in social media"):
  with members:
    - "spreading processes in multilayer networks"
    - "multiple routes transmitted epidemics on multiplex networks"
    - "phase transitions in spectral community detection"
    - "diffusion of real time information in social physical networks"
    - "structure and dynamics of information pathways in online media"
    - "uncovering the temporal dynamics of diffusion 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)