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: "deep irt make deep learning based knowledge tracing explainable using item response theory"
  Abstract: Deep learning based knowledge tracing model has been shown to outperform traditional knowledge tracing model without the need for human-engineered features, yet its parameters and representations have long been criticized for not being explainable. In this paper, we propose Deep-IRT which is a synthesis of the item response theory (IRT) model and a knowledge tracing model that is based on the deep neural network architecture called dynamic key-value memory network (DKVMN) to make deep learning based knowledge tracing explainable. Specifically, we use the DKVMN model to process the student's learning trajectory and estimate the student ability level and the item difficulty level over time. Then, we use the IRT model to estimate the probability that a student will answer an item correctly using the estimated student ability and the item difficulty. Experiments show that the Deep-IRT model retains the performance of the DKVMN model, while it provides a direct psychological interpretation of both students and items.

Connected through hyperedge HE-1 (induced by source paper "domain adaption for knowledge tracing"):
  with members:
    - "enhancing item response theory for cognitive diagnosis"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "addressing two problems in deep knowledge tracing via prediction consistent regularization"
    - "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 "variational item response theory fast accurate and expressive"):
  with members:
    - "the stanford acuity test a probabilistic approach for precise visual acuity testing"
    - "neural autoregressive flows"
    - "pyro deep universal probabilistic programming"
    - "multimodal generative models for scalable weakly supervised learning"
    - "attention is all you need"
    - "improving variational auto encoders using householder flow"
    - "dynamic key value memory networks for knowledge tracing"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "deep trustworthy knowledge tracing"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "addressing two problems in deep knowledge tracing via prediction consistent regularization"
    - "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-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"
    - "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"
    - "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.LG(Machine Learning)