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: "ekt exercise aware knowledge tracing for student performance prediction"
  Abstract: For offering proactive services to students in intelligent education, one of the fundamental tasks is predicting their performance (e.g., scores) on future exercises, where it is necessary to track each student's knowledge acquisition during her exercising activities. However, existing approaches can only exploit the exercising records of students, and the problem of extracting rich information existed in the exercise's materials (e.g., knowledge concepts, exercise content) to achieve both precise predictions of student performance and interpretable analysis of knowledge acquisition remains underexplored. In this paper, we present a holistic study of student performance prediction. To directly achieve the primary goal of prediction, we first propose a general Exercise-Enhanced Recurrent Neural Network (EERNN) framework by exploring both student's records and the exercise contents. In EERNN, we simply summarize each student's state into an integrated vector and trace it with a recurrent neural network, where we design a bidirectional LSTM to learn the encoding of each exercise's content. For making predictions, we propose two implementations under EERNN with different strategies, i.e., EERNNM with Markov property and EERNNA with Attention mechanism. Then, to explicitly track student's knowledge acquisition on multiple knowledge concepts, we extend EERNN to an explainable Exercise-aware Knowledge Tracing (EKT) by incorporating the knowledge concept effects, where the student's integrated state vector is extended to a knowledge state matrix. In EKT, we further develop a memory network for quantifying how much each exercise can affect the mastery of students on concepts during the exercising process. Finally, we conduct extensive experiments on large-scale real-world data. The results demonstrate the prediction effectiveness of two frameworks as well as the superior interpretability of EKT.

Connected through hyperedge HE-1 (induced by source paper "assessment modeling fundamental pre training tasks for interactive educational systems"):
  with members:
    - "ednet a large scale hierarchical dataset in education"
    - "quesnet a unified representation for heterogeneous test questions"
    - "creating a neural pedagogical agent by jointly learning to review and assess"
    - "a comprehensive study of imagenet pre training for historical document image analysis"
    - "exploiting cognitive structure for adaptive learning"
    - "a self attentive model for knowledge tracing"
    - "wav2vec unsupervised pre training for speech recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "towards an appropriate query key and value computation for knowledge tracing"):
  with members:
    - "ednet a large scale hierarchical dataset in education"
    - "creating a neural pedagogical agent by jointly learning to review and assess"
    - "a self attentive model for knowledge tracing"
    - "attention is all you need"
    - "effective approaches to attention based neural machine translation"
    - "deep residual learning for image recognition"
    - "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"
    - "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"
    - "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 "neural cognitive diagnosis for intelligent education systems"):
  with members:
    - "hierarchical context enabled recurrent neural network for recommendation"
    - "transcribing content from structural images with spotlight mechanism"
    - "zero shot learning for code education rubric sampling with deep learning inference"
    - "character level convolutional networks for text classification"
    - "engaging with massive online courses"

Connected through hyperedge HE-5 (induced by source paper "ednet a large scale hierarchical dataset in education"):
  with members:
    - "creating a neural pedagogical agent by jointly learning to review and assess"
    - "exploiting cognitive structure for adaptive learning"
    - "a self attentive model for knowledge tracing"
    - "knowledge tracing with sequential key value memory networks"
    - "personalized course sequence recommendations"
    - "dynamic key value memory networks for knowledge tracing"

Connected through hyperedge HE-6 (induced by source paper "interpretable cognitive diagnosis with neural network"):
  with members:
    - "hierarchical context enabled recurrent neural network for recommendation"
    - "navigating with graph representations for fast and scalable decoding of neural language models"
    - "transcribing content from structural images with spotlight mechanism"
    - "zero shot learning for code education rubric sampling with deep learning inference"
    - "unsupervised image to image translation networks"
    - "engaging with massive online courses"

Connected through hyperedge HE-7 (induced by source paper "deep technology tracing for high tech companies"):
  with members:
    - "a primer on neural network models for natural language processing"
    - "speech recognition with deep recurrent neural networks"
    - "prediction of emerging technologies based on analysis of the us patent citation network"
    - "adadelta an adaptive learning rate method"

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.CY(Computers and Society)