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: "back to the basics bayesian extensions of irt outperform neural networks for proficiency estimation"
  Abstract: Estimating student proficiency is an important task for computer based learning systems. We compare a family of IRT-based proficiency estimation methods to Deep Knowledge Tracing (DKT), a recently proposed recurrent neural network model with promising initial results. We evaluate how well each model predicts a student's future response given previous responses using two publicly available and one proprietary data set. We find that IRT-based methods consistently matched or outperformed DKT across all data sets at the finest level of content granularity that was tractable for them to be trained on. A hierarchical extension of IRT that captured item grouping structure performed best overall. When data sets included non-trivial autocorrelations in student response patterns, a temporal extension of IRT improved performance over standard IRT while the RNN-based method did not. We conclude that IRT-based models provide a simpler, better-performing alternative to existing RNN-based models of student interaction data while also affording more interpretability and guarantees due to their formulation as Bayesian probabilistic models.

Connected through hyperedge HE-1 (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"
    - "addressing two problems in deep knowledge tracing via prediction consistent regularization"
    - "dynamic key value memory networks for knowledge tracing"

Connected through hyperedge HE-2 (induced by source paper "knowledge query network for knowledge tracing how knowledge interacts with skills"):
  with members:
    - "addressing two problems in deep knowledge tracing via prediction consistent regularization"
    - "how deep is knowledge tracing"
    - "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-3 (induced by source paper "das3h modeling student learning and forgetting for optimally scheduling distributed practice of skills"):
  with members:
    - "knowledge tracing machines factorization machines for knowledge tracing"
    - "deep knowledge tracing and dynamic student classification for knowledge tracing"
    - "t skirt online estimation of student proficiency in an adaptive learning system"
    - "dynamic key value memory networks for knowledge tracing"
    - "unbounded human learning optimal scheduling for spaced repetition"

Connected through hyperedge HE-4 (induced by source paper "deep irt make deep learning based knowledge tracing explainable using item response theory"):
  with members:
    - "how deep is knowledge tracing"
    - "dynamic key value memory networks for knowledge tracing"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (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"
    - "addressing two problems in deep knowledge tracing via prediction consistent regularization"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "addressing two problems in deep knowledge tracing via prediction consistent regularization"):
  with members:
    - "how deep is knowledge tracing"
    - "modelling student behavior using granular large scale action data from a mooc"
    - "dynamic key value memory networks for knowledge tracing"
    - "a critical review of recurrent neural networks for sequence learning"

Connected through hyperedge HE-7 (induced by source paper "deep knowledge tracing and dynamic student classification for knowledge tracing"):
  with members:
    - "t skirt online estimation of student proficiency in an adaptive learning system"

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)