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 "deep trustworthy knowledge tracing"):
  Abstract: Knowledge tracing (KT), a key component of an intelligent tutoring system, is a machine learning technique that estimates the mastery level of a student based on his/her past performance. The objective of KT is to predict a student's response to the next question. Compared with traditional KT models, deep learning-based KT (DLKT) models show better predictive performance because of the representation power of deep neural networks. Various methods have been proposed to improve the performance of DLKT, but few studies have been conducted on the reliability of DLKT. In this work, we claim that the existing DLKTs are not reliable in real education environments. To substantiate the claim, we show limitations of DLKT from various perspectives such as knowledge state update failure, catastrophic forgetting, and non-interpretability. We then propose a novel regularization to address these problems. The proposed method allows us to achieve trustworthy DLKT. In addition, the proposed model which is trained on scenarios with forgetting can also be easily extended to scenarios without forgetting.

Includes members:
  Member "continual lifelong learning with neural networks a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "deep irt make deep learning based knowledge tracing explainable using item response theory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "domain adaption for knowledge tracing")
  Member "addressing two problems in deep knowledge tracing via prediction consistent regularization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "domain adaption for knowledge tracing")
  Member "one shot learning with memory augmented neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "incremental few shot object detection")
  Member "dynamic key value memory networks for knowledge tracing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "what changed your mind the roles of dynamic topics and discourse in argumentation process")
  Member "a critical review of recurrent neural networks for sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ten social dimensions of conversations and relationships")
  Member "neural turing machines":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mast a memory augmented self supervised tracker")

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.AI(Artificial Intelligence)