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: "delving deeper into mooc student dropout prediction"
  Abstract: In order to obtain reliable accuracy estimates for automatic MOOC dropout predictors, it is important to train and test them in a manner consistent with how they will be used in practice. Yet most prior research on MOOC dropout prediction has measured test accuracy on the same course used for training the classifier, which can lead to overly optimistic accuracy estimates. In order to understand better how accuracy is affected by the training+testing regime, we compared the accuracy of a standard dropout prediction architecture (clickstream features + logistic regression) across 4 different training paradigms. Results suggest that (1) training and testing on the same course ("post-hoc") can overestimate accuracy by several percentage points; (2) dropout classifiers trained on proxy labels based on students' persistence are surprisingly competitive with post-hoc training (87.33% versus 90.20% AUC averaged over 8 weeks of 40 HarvardX MOOCs); and (3) classifier performance does not vary significantly with the academic discipline. Finally, we also research new dropout prediction architectures based on deep, fully-connected, feed-forward neural networks and find that (4) networks with as many as 5 hidden layers can statistically significantly increase test accuracy over that of logistic regression.

Connected through hyperedge HE-1 (induced by source paper "deep attentive study session dropout prediction in mobile learning environment"):
  with members:
    - "ednet a large scale hierarchical dataset in education"
    - "modelling sequential music track skips using a multi rnn approach"
    - "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"
    - "predicting learning status in moocs using lstm"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "dropout prediction over weeks in moocs by learning representations of clicks and videos"):
  with members:
    - "gritnet student performance prediction with deep learning"
    - "limits of end to end learning"
    - "likely to stop predicting stopout in massive open online courses"
    - "your click decides your fate leveraging clickstream patterns in mooc videos to infer students information processing and attrition behavior"
    - "capturing attrition intensifying structural traits from didactic interaction sequences of mooc learners"

Connected through hyperedge HE-3 (induced by source paper "connectionist recommendation in the wild on the utility and scrutability of neural networks for personalized course guidance"):
  with members:
    - "a map of knowledge"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "analysis of student behaviour in habitable worlds using continuous representation visualization"
    - "improved recurrent neural networks for session based recommendations"
    - "item2vec neural item embedding for collaborative filtering"
    - "infusing collaborative recommenders with distributed representations"
    - "a generic coordinate descent framework for learning from implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "connectionist recommendation in the wild"):
  with members:
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "improved recurrent neural networks for session based recommendations"
    - "a generic coordinate descent framework for learning from implicit feedback"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "session based recommendations with recurrent neural networks"
    - "draw a recurrent neural network for image generation"
    - "a primer on neural network models for natural language processing"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "student success prediction in moocs"):
  with members:
    - "morf a framework for mooc predictive modeling and replication at scale"
    - "predicting performance on mooc assessments using multi regression models"
    - "why should i trust you explaining the predictions of any classifier"
    - "mining mooc clickstreams on the relationship between learner video watching behavior and performance"
    - "towards feature engineering at scale for data from massive open online courses"
    - "likely to stop predicting stopout in massive open online courses"
    - "your click decides your fate inferring information processing and attrition behavior from mooc video clickstream interactions"
    - "capturing attrition intensifying structural traits from didactic interaction sequences of mooc learners"

Connected through hyperedge HE-6 (induced by source paper "morf a framework for predictive modeling and replication at scale with privacy restricted mooc data"):
  with members:
    - "enabling end to end machine learning replicability a case study in educational data mining"
    - "student success prediction in moocs"
    - "a collaborative approach to computational reproducibility"
    - "towards feature engineering at scale for data from massive open online courses"

Connected through hyperedge HE-7 (induced by source paper "domain adaptation for real time student performance prediction"):
  with members:
    - "gritnet student performance prediction with deep learning"

Connected through hyperedge HE-8 (induced by source paper "gritnet 2 real time student performance prediction with domain adaptation"):
  with members:
    - "gritnet student performance prediction with deep learning"

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)