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: "relating modularity maximization and stochastic block models in multilayer networks"
  Abstract: Characterizing large-scale organization in networks, including multilayer networks, is one of the most prominent topics in network science and is important for many applications. One type of mesoscale feature is community structure, in which sets of nodes are densely connected internally but sparsely connected to other dense sets of nodes. Two of the most popular approaches for community detection are to maximize an objective function called "modularity" and to perform statistical inference using stochastic block models. Generalizing work by Newman on monolayer networks (Physical Review E 94, 052315), we show in multilayer networks that maximizing modularity is equivalent, under certain conditions, to maximizing the posterior probability of community assignments under a suitably chosen stochastic block model. We derive versions of this equivalence for various types of multilayer structure, including temporal, multiplex, and multilevel networks. We consider cases in which the key parameters are constant, as well as ones in which they vary across layers; in the latter case, this yields a novel, layer-weighted version of the modularity function. Our results also help address a longstanding difficulty of multilayer modularity-maximization algorithms, which require the specification of two sets of tuning parameters that have been difficult to choose in practice. We show how to perform this parameter selection in a statistically-grounded way, and we demonstrate the effectiveness of our approach on both synthetic and empirical networks.

Connected through hyperedge HE-1 (induced by source paper "role detection in bicycle sharing networks using multilayer stochastic block models"):
  with members:
    - "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"
    - "a framework for the construction of generative models for mesoscale structure in multilayer 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-2 (induced by source paper "supracentrality analysis of temporal networks with directed interlayer coupling"):
  with members:
    - "tunable eigenvector based centralities for multiplex and temporal networks"
    - "a new framework for centrality measures in multiplex networks"
    - "distributed algorithms for computation of centrality measures in complex networks"
    - "node and layer eigenvector centralities for multiplex networks"
    - "post processing partitions to identify domains of modularity optimization"
    - "a new framework for dynamical models on multiplex networks"
    - "block matrix formulations for evolving networks"
    - "pagerank beyond the web"

Connected through hyperedge HE-3 (induced by source paper "modularity belief propagation on multilayer networks to detect significant community structure"):
  with members:
    - "post processing partitions to identify domains of modularity optimization"
    - "the many facets of community detection in complex networks"
    - "the ground truth about metadata and community detection in networks"
    - "graph spectra and the detectability of community structure in 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)