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: "how deep is knowledge tracing"
  Abstract: In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better. This tension has recently surfaced in the realm of educational data mining, where a deep learning approach to predicting students' performance as they work through a series of exercises---termed deep knowledge tracing or DKT---has demonstrated a stunning performance advantage over the mainstay of the field, Bayesian knowledge tracing or BKT. In this article, we attempt to understand the basis for DKT's advantage by considering the sources of statistical regularity in the data that DKT can leverage but which BKT cannot. We hypothesize four forms of regularity that BKT fails to exploit: recency effects, the contextualized trial sequence, inter-skill similarity, and individual variation in ability. We demonstrate that when BKT is extended to allow it more flexibility in modeling statistical regularities---using extensions previously proposed in the literature---BKT achieves a level of performance indistinguishable from that of DKT. We argue that while DKT is a powerful, useful, general-purpose framework for modeling student learning, its gains do not come from the discovery of novel representations---the fundamental advantage of deep learning. To answer the question posed in our title, knowledge tracing may be a domain that does not require `depth'; shallow models like BKT can perform just as well and offer us greater interpretability and explanatory power.

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"
    - "addressing two problems in deep knowledge tracing via prediction consistent regularization"
    - "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 "knowledge tracing with sequential key value memory networks"):
  with members:
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "learning to skim text"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "dynamic key value memory networks for knowledge tracing"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-3 (induced by source paper "program classification using gated graph attention neural network for online programming service"):
  with members:
    - "a survey of machine learning for big code and naturalness"
    - "learning to represent programs with graphs"
    - "learning python code suggestion with a sparse pointer network"
    - "cross language learning for program classification using bilateral tree based convolutional neural networks"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep api learning"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "scaling up psychology via scientific regret minimization a case study in moral decision making"):
  with members:
    - "using machine learning to guide cognitive modeling a case study in moral reasoning"
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "a computational model of commonsense moral decision making"
    - "on calibration of modern neural networks"
    - "the theory is predictive but is it complete an application to human perception of randomness"
    - "evaluating and improving the correspondence between deep neural networks and human representations"

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

Connected through hyperedge HE-7 (induced by source paper "deep irt make deep learning based knowledge tracing explainable using item response theory"):
  with members:
    - "back to the basics bayesian extensions of irt outperform neural networks for proficiency estimation"
    - "dynamic key value memory networks for knowledge tracing"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "using machine learning to guide cognitive modeling a case study in moral reasoning"):
  with members:
    - "beyond imitation zero shot task transfer on robots by learning concepts as cognitive programs"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"

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)