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 "das3h modeling student learning and forgetting for optimally scheduling distributed practice of skills"):
  Abstract: Spaced repetition is among the most studied learning strategies in the cognitive science literature. It consists in temporally distributing exposure to an information so as to improve long-term memorization. Providing students with an adaptive and personalized distributed practice schedule would benefit more than just a generic scheduler. However, the applicability of such adaptive schedulers seems to be limited to pure memorization, e.g. flashcards or foreign language learning. In this article, we first frame the research problem of optimizing an adaptive and personalized spaced repetition scheduler when memorization concerns the application of underlying multiple skills. To this end, we choose to rely on a student model for inferring knowledge state and memory dynamics on any skill or combination of skills. We argue that no knowledge tracing model takes both memory decay and multiple skill tagging into account for predicting student performance. As a consequence, we propose a new student learning and forgetting model suited to our research problem: DAS3H builds on the additive factor models and includes a representation of the temporal distribution of past practice on the skills involved by an item. In particular, DAS3H allows the learning and forgetting curves to differ from one skill to another. Finally, we provide empirical evidence on three real-world educational datasets that DAS3H outperforms other state-of-the-art EDM models. These results suggest that incorporating both item-skill relationships and forgetting effect improves over student models that consider one or the other.

Includes members:
  Member "knowledge tracing machines factorization machines for knowledge tracing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "truelearn a family of bayesian algorithms to match lifelong learners to open educational resources")
  Member "deep knowledge tracing and dynamic student classification for knowledge tracing":
    also connected through other hyperedges:
      (none)
  Member "t skirt online estimation of student proficiency in an adaptive learning system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep knowledge tracing and dynamic student classification for knowledge tracing")
  Member "back to the basics bayesian extensions of irt outperform neural networks for proficiency estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "r2de a nlp approach to estimating irt parameters of newly generated questions")
  Member "dynamic key value memory networks for knowledge tracing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "what changed your mind the roles of dynamic topics and discourse in argumentation process")
  Member "unbounded human learning optimal scheduling for spaced repetition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "teaching multiple concepts to forgetful learners")

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.CY(Computers and Society)