human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "addressing two problems in deep knowledge tracing via prediction consistent regularization"
  Abstract: Knowledge tracing is one of the key research areas for empowering personalized education. It is a task to model students' mastery level of a knowledge component (KC) based on their historical learning trajectories. In recent years, a recurrent neural network model called deep knowledge tracing (DKT) has been proposed to handle the knowledge tracing task and literature has shown that DKT generally outperforms traditional methods. However, through our extensive experimentation, we have noticed two major problems in the DKT model. The first problem is that the model fails to reconstruct the observed input. As a result, even when a student performs well on a KC, the prediction of that KC's mastery level decreases instead, and vice versa. Second, the predicted performance for KCs across time-steps is not consistent. This is undesirable and unreasonable because student's performance is expected to transit gradually over time. To address these problems, we introduce regularization terms that correspond to reconstruction and waviness to the loss function of the original DKT model to enhance the consistency in prediction. Experiments show that the regularized loss function effectively alleviates the two problems without degrading the original task of DKT.

Connected through hyperedge HE-1 (induced by source paper "domain adaption for knowledge tracing"):
  with members:
    - "enhancing item response theory for cognitive diagnosis"
    - "deep irt make deep learning based knowledge tracing explainable using item response theory"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "how deep is knowledge tracing"
    - "deep transfer learning with joint adaptation networks"
    - "dynamic key value memory networks for knowledge tracing"
    - "bidirectional lstm crf models for sequence tagging"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "r2de a nlp approach to estimating irt parameters of newly generated questions"):
  with members:
    - "knowledge query network for knowledge tracing how knowledge interacts with skills"
    - "deep knowledge tracing with side information"
    - "ekt exercise aware knowledge tracing for student performance prediction"
    - "knowledge tracing with sequential key value memory networks"
    - "deep irt make deep learning based knowledge tracing explainable using item response theory"
    - "back to the basics bayesian extensions of irt outperform neural networks for proficiency estimation"
    - "dynamic key value memory networks for knowledge tracing"

Connected through hyperedge HE-3 (induced by source paper "what s happened in mooc posts analysis knowledge tracing and peer feedbacks a review"):
  with members:
    - "a self attentive model for knowledge tracing"
    - "roberta a robustly optimized bert pretraining approach"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "gritnet 2 real time student performance prediction with domain adaptation"
    - "comment ranking diversification in forum discussions"
    - "towards feature engineering at scale for data from massive open online courses"

Connected through hyperedge HE-4 (induced by source paper "predicting student performance in interactive online question pools using mouse interaction features"):
  with members:
    - "effective feature learning with unsupervised learning for improving the predictive models in massive open online courses"
    - "an empirical analysis of feature engineering for predictive modeling"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-5 (induced by source paper "knowledge query network for knowledge tracing how knowledge interacts with skills"):
  with members:
    - "how deep is knowledge tracing"
    - "back to the basics bayesian extensions of irt outperform neural networks for proficiency estimation"
    - "dynamic key value memory networks for knowledge tracing"
    - "adam a method for stochastic optimization"
    - "neural turing machines"
    - "recurrent neural network regularization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "a self attentive model for knowledge tracing"):
  with members:
    - "self attentive sequential recommendation"
    - "attention is all you need"
    - "one shot learning with memory augmented neural networks"
    - "how deep is knowledge tracing"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "deep trustworthy knowledge tracing"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "deep irt make deep learning based knowledge tracing explainable using item response theory"
    - "one shot learning with memory augmented neural networks"
    - "dynamic key value memory networks for knowledge tracing"
    - "a critical review of recurrent neural networks for sequence learning"
    - "neural turing machines"

Connected through hyperedge HE-8 (induced by source paper "memory augmented neural networks for knowledge tracing from the perspective of learning and forgetting"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "deep irt make deep learning based knowledge tracing explainable using item response theory"
    - "reinforcement learning based recommender system using biclustering technique"
    - "deep reinforcement learning for list wise recommendations"
    - "one shot learning with memory augmented neural networks"
    - "how deep is knowledge tracing"
    - "latent skill embedding for personalized lesson sequence recommendation"
    - "back to the basics bayesian extensions of irt outperform neural networks for proficiency estimation"

Question: Please tell me which class the center node 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)