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: "dynamic key value memory networks for knowledge tracing"
  Abstract: Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities. One important purpose of KT is to personalize the practice sequence to help students learn knowledge concepts efficiently. However, existing methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing either model knowledge state for each predefined concept separately or fail to pinpoint exactly which concepts a student is good at or unfamiliar with. To solve these problems, this work introduces a new model called Dynamic Key-Value Memory Networks (DKVMN) that can exploit the relationships between underlying concepts and directly output a student's mastery level of each concept. Unlike standard memory-augmented neural networks that facilitate a single memory matrix or two static memory matrices, our model has one static matrix called key, which stores the knowledge concepts and the other dynamic matrix called value, which stores and updates the mastery levels of corresponding concepts. Experiments show that our model consistently outperforms the state-of-the-art model in a range of KT datasets. Moreover, the DKVMN model can automatically discover underlying concepts of exercises typically performed by human annotations and depict the changing knowledge state of a student.

Connected through hyperedge HE-1 (induced by source paper "what changed your mind the roles of dynamic topics and discourse in argumentation process"):
  with members:
    - "unsupervised dialog structure learning"
    - "what you say and how you say it joint modeling of topics and discourse in microblog conversations"
    - "what gets echoed understanding the pointers in explanations of persuasive arguments"
    - "topic memory networks for short text classification"
    - "a joint model of conversational discourse and latent topics on microblogs"
    - "attentive interaction model modeling changes in view in argumentation"
    - "joint modeling of content and discourse relations in dialogues"
    - "winning on the merits the joint effects of content and style on debate outcomes"

Connected through hyperedge HE-2 (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"
    - "ekt exercise aware knowledge tracing for student performance prediction"
    - "wav2vec unsupervised pre training for speech recognition"

Connected through hyperedge HE-3 (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"
    - "how deep is knowledge tracing"
    - "deep transfer learning with joint adaptation networks"
    - "bidirectional lstm crf models for sequence tagging"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (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"
    - "deep irt make deep learning based knowledge tracing explainable using item response theory"
    - "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"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (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"
    - "back to the basics bayesian extensions of irt outperform neural networks for proficiency estimation"

Connected through hyperedge HE-6 (induced by source paper "memory augmented graph neural networks for sequential recommendation"):
  with members:
    - "quantifying long range dependence in language and user behavior to improve rnns"
    - "signed distance based deep memory recommender"
    - "gated attentive autoencoder for content aware recommendation"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "relational inductive biases deep learning and graph networks"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"

Connected through hyperedge HE-7 (induced by source paper "ekt exercise aware knowledge tracing for student performance prediction"):
  with members:
    - "transcribing content from structural images with spotlight mechanism"
    - "stable prediction across unknown environments"
    - "a survey on network embedding"
    - "multi behavioral sequential prediction with recurrent log bilinear model"
    - "dynamic memory networks for visual and textual question answering"
    - "end to end memory networks"
    - "lstm based deep learning models for non factoid answer selection"
    - "ask me anything dynamic memory networks for natural language processing"

Connected through hyperedge HE-8 (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"
    - "how deep is knowledge tracing"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"
    - "long term recurrent convolutional networks for visual recognition and description"

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)