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: "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"
  Abstract: When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.

Connected through hyperedge HE-1 (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"
    - "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"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-2 (induced by source paper "exploring the intersections of web science and accessibility"):
  with members:
    - "adapting the hypercube model to archive deferred representations and their descendants"
    - "archiving deferred representations using a two tiered crawling approach"
    - "harnessing the deep web present and future"
    - "an architectural style for ajax"

Connected through hyperedge HE-3 (induced by source paper "query for architecture click through military comparing the roles of search and navigation on wikipedia"):
  with members:
    - "toktrack a complete token provenance and change tracking dataset for the english wikipedia"
    - "why we read wikipedia"
    - "analysing timelines of national histories across wikipedia editions a comparative computational approach"
    - "improving website hyperlink structure using server logs"
    - "what makes a link successful on wikipedia"
    - "detecting memory and structure in human navigation patterns using markov chain models of varying order"

Connected through hyperedge HE-4 (induced by source paper "retrospective higher order markov processes for user trails"):
  with members:
    - "the spacey random walk a stochastic process for higher order data"
    - "linear additive markov processes"
    - "higher order organization of complex networks"
    - "general tensor spectral co clustering for higher order data"
    - "the new data and new challenges in multimedia research"
    - "tensor spectral clustering for partitioning higher order network structures"
    - "tribeflow mining predicting user trajectories"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

Connected through hyperedge HE-5 (induced by source paper "mixedtrails bayesian hypothesis comparison on heterogeneous sequential data"):
  with members:
    - "what makes a link successful on wikipedia"
    - "tribeflow mining predicting user trajectories"
    - "detecting memory and structure in human navigation patterns using markov chain models of varying order"

Connected through hyperedge HE-6 (induced by source paper "linear additive markov processes"):
  with members:
    - "recurrent neural network regularization"
    - "evaluating variable length markov chain models for analysis of user web navigation sessions"

Connected through hyperedge HE-7 (induced by source paper "how users explore ontologies on the web a study of ncbo s bioportal usage logs"):
  with members:
    - "discovering and characterizing mobility patterns in urban spaces a study of manhattan taxi data"
    - "how to apply markov chains for modeling sequential edit patterns in collaborative ontology engineering projects"
    - "discovering beaten paths in collaborative ontology engineering projects using markov chains"
    - "enabling semantic analysis of user browsing patterns in the web of data"
    - "user modeling combining access logs page content and semantics"
    - "evaluating variable length markov chain models for analysis of user web navigation sessions"

Connected through hyperedge HE-8 (induced by source paper "what makes a link successful on wikipedia"):
  with members:
    - "mixedtrails bayesian hypothesis comparison on heterogeneous sequential data"
    - "improving website hyperlink structure using server logs"
    - "mining missing hyperlinks from human navigation traces a case study of wikipedia"
    - "detecting memory and structure in human navigation patterns using markov chain models of varying order"

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)