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: "contextualised browsing in a digital library s living lab"
  Abstract: Contextualisation has proven to be effective in tailoring search results towards the users' information need. While this is true for a basic query search, the usage of contextual session information during exploratory search especially on the level of browsing has so far been underexposed in research. In this paper, we present two approaches that contextualise browsing on the level of structured metadata in a Digital Library (DL), (1) one variant bases on document similarity and (2) one variant utilises implicit session information, such as queries and different document metadata encountered during the session of a users. We evaluate our approaches in a living lab environment using a DL in the social sciences and compare our contextualisation approaches against a non-contextualised approach. For a period of more than three months we analysed 47,444 unique retrieval sessions that contain search activities on the level of browsing. Our results show that a contextualisation of browsing significantly outperforms our baseline in terms of the position of the first clicked item in the result set. The mean rank of the first clicked document (measured as mean first relevant - MFR) was 4.52 using a non-contextualised ranking compared to 3.04 when re-ranking the result lists based on similarity to the previously viewed document. Furthermore, we observed that both contextual approaches show a noticeably higher click-through rate. A contextualisation based on document similarity leads to almost twice as many document views compared to the non-contextualised ranking.

Connected through hyperedge HE-1 (induced by source paper "online evaluations for everyone mr dlib s living lab for scholarly recommendations"):
  with members:
    - "rard ii the 2nd related article recommendation dataset"
    - "the architecture of mr dlib s scientific recommender system api"
    - "rard ii the 94 million related article recommendation dataset"
    - "graph embedding for citation recommendation"
    - "mr dlib recommendations as a service raas for academia"
    - "the conference paper assignment problem using order weighted averages to assign indivisible goods"
    - "towards effective research recommender systems for repositories"
    - "evaluation as a service overview and outlook"

Connected through hyperedge HE-2 (induced by source paper "analysis of search stratagem utilisation"):
  with members:
    - "investigating exploratory search activities based on the stratagem level in digital libraries"
    - "analysis of footnote chasing and citation searching in an academic search engine"
    - "a complete year of user retrieval sessions in a social sciences academic search engine"
    - "a usefulness based approach for measuring the local and global effect of iir services"
    - "extending search facilities via bibliometric enhanced stratagems"
    - "scientometrics and information retrieval weak links revitalized"
    - "science models as value added services for scholarly information systems"

Connected through hyperedge HE-3 (induced by source paper "data search 18 searching data on the web"):
  with members:
    - "understanding data search as a socio technical practice"
    - "searching data a review of observational data retrieval practices in selected disciplines"
    - "learning latent vector spaces for product search"

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.IR(Information Retrieval)