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: "from relational data to graphs inferring significant links using generalized hypergeometric ensembles"
  Abstract: The inference of network topologies from relational data is an important problem in data analysis. Exemplary applications include the reconstruction of social ties from data on human interactions, the inference of gene co-expression networks from DNA microarray data, or the learning of semantic relationships based on co-occurrences of words in documents. Solving these problems requires techniques to infer significant links in noisy relational data. In this short paper, we propose a new statistical modeling framework to address this challenge. The framework builds on generalized hypergeometric ensembles, a class of generative stochastic models that give rise to analytically tractable probability spaces of directed, multi-edge graphs. We show how this framework can be used to assess the significance of links in noisy relational data. We illustrate our method in two data sets capturing spatio-temporal proximity relations between actors in a social system. The results show that our analytical framework provides a new approach to infer significant links from relational data, with interesting perspectives for the mining of data on social systems.

Connected through hyperedge HE-1 (induced by source paper "detecting path anomalies in time series data on networks"):
  with members:
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "mixedtrails bayesian hypothesis comparison on heterogeneous sequential data"
    - "modelling sequences and temporal networks with dynamic community structures"
    - "representing higher order dependencies in networks"
    - "keeping it short and simple summarising complex event sequences with multivariate patterns"
    - "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-2 (induced by source paper "transforming collaboration data into network layers for enhanced analytics"):
  with members:
    - "networks of collaborations hypergraph modeling and visualisation"
    - "the privacy exposure problem in mobile location based services"
    - "social network generation and role determination based on smartphone data"

Connected through hyperedge HE-3 (induced by source paper "network structure from rich but noisy data"):
  with members:
    - "structural inference for uncertain networks"
    - "network structure inference a survey motivations methods and applications"
    - "measuring large scale social networks with high resolution"
    - "a heuristic approach to author name disambiguation in bibliometrics databases for large scale research assessments"

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)