human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "data driven unsupervised clustering of online learner behaviour"):
  Abstract: The widespread adoption of online courses opens opportunities for the analysis of learner behaviour and for the optimisation of web-based material adapted to observed usage. Here we introduce a mathematical framework for the analysis of time series collected from online engagement of learners, which allows the identification of clusters of learners with similar online behaviour directly from the data, i.e., the groups of learners are not pre-determined subjectively but emerge algorithmically from the analysis and the data.The method uses a dynamic time warping kernel to create a pairwise similarity between time series of learner actions, and combines it with an unsupervised multiscale graph clustering algorithm to cluster groups of learners with similar patterns of behaviour. We showcase our approach on online engagement data of adult learners taking six web-based courses as part of a post-graduate degree at Imperial Business School. Our analysis identifies clusters of learners with statistically distinct patterns of engagement, ranging from distributed to massed learning, with different levels of adherence to pre-planned course structure and/or task completion, and also revealing outlier learners with highly sporadic behaviour. A posteriori comparison with performance showed that, although the majority of low-performing learners are part of in the massed learning cluster, the high performing learners are distributed across clusters with different traits of online engagement. We also show that our methodology is able to identify low performing learners more accurately than common classification methods based on raw statistics extracted from the data.

Includes members:
  Member "likely to stop predicting stopout in massive open online courses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dropout prediction over weeks in moocs via interpretable multi layer representation learning")
  Member "an empirical evaluation of similarity measures for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an overview of distance and similarity functions for structured data")
  Member "finding role communities in directed networks using role based similarity markov stability and the relaxed minimum spanning tree":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semi supervised classification on graphs using explicit diffusion dynamics")

Question: Please tell me which class the source paper 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)