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 "assessment modeling fundamental pre training tasks for interactive educational systems"):
  Abstract: Interactive Educational Systems (IESs) have developed rapidly in recent years to address the issue of quality and affordability of education. Analogous to other domains in AI, there are specific tasks of AIEd for which labels are scarce. For instance, labels like exam score and grade are considered important in educational and social context. However, obtaining the labels is costly as they require student actions taken outside the system. Likewise, while student events like course dropout and review correctness are automatically recorded by IESs, they are few in number as the events occur sporadically in practice. A common way of circumventing the label-scarcity problem is the pre-train/fine-tine method. Accordingly, existing works pre-train a model to learn representations of contents in learning items. However, such methods fail to utilize the student interaction data available and model student learning behavior. To this end, we propose assessment modeling, fundamental pre-training tasks for IESs. An assessment is a feature of student-system interactions which can act as pedagogical evaluation, such as student response correctness or timeliness. Assessment modeling is the prediction of assessments conditioned on the surrounding context of interactions. Although it is natural to pre-train interactive features available in large amount, narrowing down the prediction targets to assessments holds relevance to the label-scarce educational problems while reducing irrelevant noises. To the best of our knowledge, this is the first work investigating appropriate pre-training method of predicting educational features from student-system interactions. While the effectiveness of different combinations of assessments is open for exploration, we suggest assessment modeling as a guiding principle for selecting proper pre-training tasks for the label-scarce educational problems.

Includes members:
  Member "ednet a large scale hierarchical dataset in education":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep attentive study session dropout prediction in mobile learning environment")
  Member "quesnet a unified representation for heterogeneous test questions":
    also connected through other hyperedges:
      (none)
  Member "creating a neural pedagogical agent by jointly learning to review and assess":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep attentive study session dropout prediction in mobile learning environment")
  Member "a comprehensive study of imagenet pre training for historical document image analysis":
    also connected through other hyperedges:
      (none)
  Member "exploiting cognitive structure for adaptive learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep attentive study session dropout prediction in mobile learning environment")
  Member "a self attentive model for knowledge tracing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep attentive study session dropout prediction in mobile learning environment")
  Member "ekt exercise aware knowledge tracing for student performance prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards an appropriate query key and value computation for knowledge tracing")
  Member "wav2vec unsupervised pre training for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning robust and multilingual speech representations")

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.LG(Machine Learning)