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 "memory augmented neural networks for knowledge tracing from the perspective of learning and forgetting"):
  Abstract: Knowledge tracing (KT) refers to a machine learning technique to assess a student's level of understanding (or knowledge state) based on the student's past performance in exercise-solving. KT accepts a series of question-answer pairs as an input and iteratively updates the knowledge state of the student, eventually returning the probability of the student solving a given question. To estimate the accurate knowledge state, a KT model should imitate the learning and forgetting mechanisms of the student. Deep learning-based KT models, proposed recently, show a higher predictive performance than traditional machine learning-based KT models due to the representative power of neural networks. The dynamic key value memory network (DKVMN), a kind of memory augmented neural network (MANN), is a state-of-the-art KT model, but it has some limitations. DKVMN does not utilize information from a current knowledge state and overestimates the amount of forgetting when updating the knowledge state. To improve the learning and forgetting mechanism of the DKVMN, we propose a knowledge tracing model that incorporates: (1) an adaptive knowledge growth depending on the current knowledge state, and (2) an additional loss term that can regularize the degree of forgetting. To measure the degree of forgetting of the KT model, we define a positive update ratio (PUR) that can complement the predictive performance metric (AUC). According to our experiments using four public benchmarks, the proposed approaches outperform the original DKVMN in terms of both AUC (predictive performance) and PUR (degree of 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 "reinforcement learning based recommender system using biclustering technique":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "jointly learning to recommend and advertise")
  Member "addressing two problems in deep knowledge tracing via prediction consistent regularization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "domain adaption for knowledge tracing")
  Member "deep reinforcement learning for list wise recommendations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for search recommendation and online advertising a survey by xiangyu zhao long xia jiliang tang and dawei yin with martin vesely as coordinator")
  Member "one shot learning with memory augmented neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "incremental few shot object detection")
  Member "how deep is knowledge tracing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "domain adaption for knowledge tracing")
  Member "latent skill embedding for personalized lesson sequence recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "will this course increase or decrease your gpa towards grade aware course recommendation")

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)