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 memory and structure in human navigation patterns using markov chain models of varying order"
  Abstract: One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.

Connected through hyperedge HE-1 (induced by source paper "quantifying engagement with citations on wikipedia"):
  with members:
    - "citation needed a taxonomy and algorithmic assessment of wikipedia s verifiability"
    - "mapping the backbone of the humanities through the eyes of wikipedia"
    - "detecting and gauging impact on wikipedia page views"
    - "eliciting new wikipedia users interests via automatically mined questionnaires for a warm welcome not a cold start"
    - "why the world reads wikipedia beyond english speakers"
    - "structuring wikipedia articles with section recommendations"
    - "amplifying the impact of open access wikipedia and the diffusion of science"
    - "why we read wikipedia"

Connected through hyperedge HE-2 (induced by source paper "hoprank how semantic structure influences teleportation in pagerank a case study on bioportal"):
  with members:
    - "how users explore ontologies on the web a study of ncbo s bioportal usage logs"
    - "random surfers on a web encyclopedia"

Connected through hyperedge HE-3 (induced by source paper "why the world reads wikipedia beyond english speakers"):
  with members:
    - "why we read wikipedia"
    - "analysing timelines of national histories across wikipedia editions a comparative computational approach"
    - "what makes a link successful on wikipedia"
    - "measuring urban deprivation from user generated content"
    - "cultures in community question answering"
    - "you are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"
    - "mining cross cultural relations from wikipedia a study of 31 european food cultures"

Connected through hyperedge HE-4 (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"
    - "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"

Connected through hyperedge HE-5 (induced by source paper "mapping higher order network flows in memory and multilayer networks with infomap"):
  with members:
    - "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"
    - "maps of sparse markov chains efficiently reveal community structure in network flows with memory"
    - "representing higher order dependencies in networks"
    - "multiaspect graphs algebraic representation and algorithms"
    - "ranking and clustering of nodes in networks with smart teleportation"

Connected through hyperedge HE-6 (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"
    - "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"

Connected through hyperedge HE-7 (induced by source paper "how php releases are adopted in the wild"):
  with members:
    - "adscape harvesting and analyzing online display ads"
    - "evaluating sliding and sticky target policies by measuring temporal drift in acyclic walks through a web archive"

Connected through hyperedge HE-8 (induced by source paper "why we read wikipedia"):
  with members:
    - "improving website hyperlink structure using server logs"
    - "user session identification based on strong regularities in inter activity time"

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)