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: "when is a network a network multi order graphical model selection in pathways and temporal networks"
  Abstract: We introduce a framework for the modeling of sequential data capturing pathways of varying lengths observed in a network. Such data are important, e.g., when studying click streams in the Web, travel patterns in transportation systems, information cascades in social networks, biological pathways, or time-stamped social interactions. While it is common to apply graph analytics and network analysis to such data, recent works have shown that temporal correlations can invalidate the results of such methods. This raises a fundamental question: When is a network abstraction of sequential data justified?Addressing this open question, we propose a framework that combines Markov chains of multiple, higher orders into a multi-layer graphical model that captures temporal correlations in pathways at multiple length scales simultaneously. We develop a model selection technique to infer the optimal number of layers of such a model and show that it outperforms baseline Markov order detection techniques. An application to eight real-world data sets on pathways and temporal networks shows that it allows to infer graphical models that capture both topological and temporal characteristics of such data. Our work highlights fallacies of network abstractions and provides a principled answer to the open question when they are justified. Generalizing network representations to multi-order graphical models, it opens perspectives for new data mining and knowledge discovery algorithms.

Connected through hyperedge HE-1 (induced by source paper "honem network embedding using higher order patterns in sequential data"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graph embedding techniques applications and performance a survey"
    - "mohone modeling higher order network effects in knowledgegraphs via network infused embeddings"
    - "detecting anomalies in sequential data with higher order networks"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "don t walk skip online learning of multi scale network embeddings"

Connected through hyperedge HE-2 (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"
    - "community discovery in dynamic networks a survey"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "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-3 (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"
    - "the mobility network of scientists analyzing temporal correlations in scientific careers"
    - "detecting path anomalies in time series data on networks"
    - "the temporal event graph"
    - "drawing dynamic graphs without timeslices"
    - "modelling sequences and temporal networks with dynamic community structures"
    - "evaluation of two interaction techniques for visualization of dynamic graphs"

Connected through hyperedge HE-4 (induced by source paper "detecting path anomalies in time series data on networks"):
  with members:
    - "from relational data to graphs inferring significant links using generalized hypergeometric ensembles"
    - "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-5 (induced by source paper "network interpolation"):
  with members:
    - "simplicial closure and higher order link prediction"
    - "choosing to grow a graph modeling network formation as discrete choice"
    - "a sampling framework for counting temporal motifs"
    - "motifs in temporal networks"
    - "dynamic stochastic blockmodels for time evolving social networks"

Connected through hyperedge HE-6 (induced by source paper "counting causal paths in big times series data on networks"):
  with members:
    - "motifs in temporal networks"
    - "node2vec scalable feature learning for networks"
    - "representing higher order dependencies in networks"
    - "temporal reachability graphs"

Connected through hyperedge HE-7 (induced by source paper "analysing time stamped co editing networks in software development teams using git2net"):
  with members:
    - "how do oss projects change in number and size a large scale analysis to test a model of project growth"
    - "categorizing bugs with social networks a case study on four open source software communities"
    - "the role of emotions in contributors activity a case study on the gentoo community"
    - "the rise and fall of a central contributor dynamics of social organization and performance in the gentoo community"

Connected through hyperedge HE-8 (induced by source paper "git2net mining time stamped co editing networks from large git repositories"):
  with members:
    - "how do oss projects change in number and size a large scale analysis to test a model of project growth"
    - "categorizing bugs with social networks a case study on four open source software communities"
    - "the rise and fall of a central contributor dynamics of social organization and performance in the gentoo community"

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)