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: "the mobility network of scientists analyzing temporal correlations in scientific careers"
  Abstract: To understand the mobility patterns of scientists, we combine two large-scale bibliographic data sets to reveal the geographical "career trajectories" of scientists and their temporal properties. Each trajectory contains, on the individual level, information about the institutions, cities and countries and the time spent there by scientists. By aggregating the individual career trajectories, we reconstruct the world network of movements of scientists, where the nodes represent cities and the links in- and outflows. We characterize the topological properties of this network by means of degree, local clustering coefficient, path length and neighbor connectivity. Then, by using multi-order graphical models, we analyze the temporal correlations of scientists' career trajectories at the country, city and institution level. We find that scientist movements at the city and country level can be correctly modeled from a static network perspective, because there are no statistically significant temporal correlations at these aggregation levels. In contrast, at the affiliation level we find that temporal correlations in scientists' career trajectories are important. That means, a memory effect in scientists' careers impacts their next affiliation. We demonstrate that these correlations can be correctly captured using a second-order network, in which nodes represent moves between two different affiliations. The finding that memory effects are only detectable at the lowest level of aggregation, i.e. the institution, implies that this is the most appropriate level to analyze the determinants of relocation decisions.

Connected through hyperedge HE-1 (induced by source paper "higher order visualization of causal structures in dynamics graphs"):
  with members:
    - "counting causal paths in big times series data on networks"
    - "evaluating network partitions through visualization"
    - "detecting path anomalies in time series data on networks"
    - "the temporal event graph"
    - "drawing dynamic graphs without timeslices"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "modelling sequences and temporal networks with dynamic community structures"
    - "evaluation of two interaction techniques for visualization of dynamic graphs"

Connected through hyperedge HE-2 (induced by source paper "the demography of the peripatetic researcher evidence on highly mobile scholars from the web of science"):
  with members:
    - "dimensions a competitor to scopus and the web of science"
    - "the many faces of mobility using bibliometric data to measure the movement of scientists"
    - "analysing scientific collaborations of new zealand institutions using scopus bibliometric data"
    - "tracing scientific mobility of early career researchers in spain and the netherlands through their publications"

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)