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: "discovering beaten paths in collaborative ontology engineering projects using markov chains"
  Abstract: Display Omitted We model usage patterns of five different ontology-engineering projects.Users work in micro-workflows and specific user-roles can be identified.Class hierarchy influences users' edit behavior.Users edit ontologies top-down, breadth-first and prefer closely related classes.Users perform property-based workflows. Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the International Classification of Diseases, which is currently under active development by the World Health Organization contains nearly 50 , 000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding the way these different stakeholders collaborate will enable us to improve editing environments that support such collaborations. In this paper, we uncover how large ontology-engineering projects, such as the International Classification of Diseases in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users frequently change after specific given ones) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.

Connected through hyperedge HE-1 (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"
    - "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"
    - "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-2 (induced by source paper "how to apply markov chains for modeling sequential edit patterns in collaborative ontology engineering projects"):
  with members:
    - "detecting memory and structure in human navigation patterns using markov chain models of varying order"
    - "evaluating variable length markov chain models for analysis of user web navigation sessions"

Connected through hyperedge HE-3 (induced by source paper "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"):
  with members:
    - "detecting memory and structure in human navigation patterns using markov chain models of varying order"
    - "partisan sharing facebook evidence and societal consequences"

Connected through hyperedge HE-4 (induced by source paper "sequential usage patterns in collaborative ontology engineering projects"):
  with members:
    - "evaluating variable length markov chain models for analysis of user web navigation sessions"

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)