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: "detecting sequences of system states in temporal networks"
  Abstract: Many time-evolving systems in nature, society and technology leave traces of the interactions within them. These interactions form temporal networks that reflect the states of the systems. In this work, we pursue a coarse-grained description of these systems by proposing a method to assign discrete states to the systems and inferring the sequence of such states from the data. Such states could, for example, correspond to a mental state (as inferred from neuroimaging data) or the operational state of an organization (as inferred by interpersonal communication). Our method combines a graph distance measure and hierarchical clustering. Using several empirical data sets of social temporal networks, we show that our method is capable of inferring the system’s states such as distinct activities in a school and a weekday state as opposed to a weekend state. We expect the methods to be equally useful in other settings such as temporally varying protein interactions, ecological interspecific interactions, functional connectivity in the brain and adaptive social networks.

Connected through hyperedge HE-1 (induced by source paper "detecting stable communities in link streams at multiple temporal scales"):
  with members:
    - "olcpm an online framework for detecting overlapping communities in dynamic social networks"
    - "community discovery in dynamic networks a survey"
    - "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-2 (induced by source paper "unsupervised network embedding for graph visualization clustering and classification"):
  with members:
    - "multi hop assortativities for network classification"
    - "fine grained attention mechanism for neural machine translation"
    - "graph embedding techniques applications and performance a survey"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "feature based classification of networks"
    - "detecting change points in the large scale structure of evolving networks"

Connected through hyperedge HE-3 (induced by source paper "brain network clustering via kernel arma modeling and the grassmannian"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "network topology inference from spectral templates"
    - "generalized louvain method for community detection in large 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)