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: "community discovery in dynamic networks a survey"
  Abstract: Several research studies have shown that complex networks modeling real-world phenomena are characterized by striking properties: (i) they are organized according to community structure, and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and fascinating problem started capturing researcher interest recently: the identification of evolving communities. Dynamic networks can be used to model the evolution of a system: nodes and edges are mutable, and their presence, or absence, deeply impacts the community structure that composes them.#R##N##R##N#This survey aims to present the distinctive features and challenges of dynamic community discovery and propose a classification of published approaches. As a “user manual,” this work organizes state-of-the-art methodologies into a taxonomy, based on their rationale, and their specific instantiation. Given a definition of network dynamics, desired community characteristics, and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers choose in which direction to orient their future research.

Connected through hyperedge HE-1 (induced by source paper "a survey of community search over big graphs"):
  with members:
    - "exploring communities in large profiled graphs"
    - "efficient algorithms for densest subgraph discovery"
    - "pp dblp modeling and generating attributed public private networks with dblp"
    - "an optimal and progressive approach to online search of top k influential communities"
    - "local community detection in dynamic networks"
    - "diversified coherent core search on multi layer graphs"
    - "network community detection a review and visual survey"
    - "local community extraction in directed networks"

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"
    - "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-3 (induced by source paper "discovering communities of community discovery"):
  with members:
    - "a survey of community search over big graphs"
    - "community structure a comparative evaluation of community detection methods"
    - "labelrank a stabilized label propagation algorithm for community detection in networks"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "clustering and community detection in directed networks a survey"
    - "demon a local first discovery method for overlapping communities"
    - "a classification for community discovery methods in complex networks"
    - "community detection via semi synchronous label propagation algorithms"

Connected through hyperedge HE-4 (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"
    - "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"
    - "a framework for the construction of generative models for mesoscale structure in multilayer 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-5 (induced by source paper "enumerating isolated cliques in temporal networks"):
  with members:
    - "listing all maximal k plexes in temporal graphs"
    - "the parameterized complexity of finding secluded solutions to some classical optimization problems on graphs"
    - "enumerating maximal cliques in link streams with durations"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "adapting the bron kerbosch algorithm for enumerating maximal cliques in temporal graphs"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "computing maximal cliques in link streams"
    - "parameterized complexity of secluded connectivity problems"

Connected through hyperedge HE-6 (induced by source paper "detecting stable communities in link streams at multiple temporal scales"):
  with members:
    - "detecting sequences of system states in temporal networks"
    - "olcpm an online framework for detecting overlapping communities in dynamic social networks"
    - "motifs in temporal networks"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "fast change point detection on dynamic social networks"
    - "detecting change points in the large scale structure of evolving networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-7 (induced by source paper "detecting sequences of system states in temporal networks"):
  with members:
    - "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"
    - "modeling complex systems with adaptive networks"

Connected through hyperedge HE-8 (induced by source paper "mining bursting communities in temporal graphs"):
  with members:
    - "distance generalized core decomposition"
    - "finding events in temporal networks segmentation meets densest subgraph discovery"
    - "mining maximal span cores from temporal networks"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "real time discovery of dense clusters in highly dynamic graphs identifying real world events in highly dynamic environments"
    - "defining and evaluating network communities based on ground truth"

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)