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: "combining generative and discriminative neural networks for sleep stages classification"
  Abstract: Sleep stages pattern provides important clues in diagnosing the presence of sleep disorder. By analyzing sleep stages pattern and extracting its features from EEG, EOG, and EMG signals, we can classify sleep stages. This study presents a novel classification model for predicting sleep stages with a high accuracy. The main idea is to combine the generative capability of Deep Belief Network (DBN) with a discriminative ability and sequence pattern recognizing capability of Long Short-term Memory (LSTM). We use DBN that is treated as an automatic higher level features generator. The input to DBN is 28 "handcrafted" features as used in previous sleep stages studies. We compared our method with other techniques which combined DBN with Hidden Markov Model (HMM).In this study, we exploit the sequence or time series characteristics of sleep dataset. To the best of our knowledge, most of the present sleep analysis from polysomnogram relies only on single instanced label (nonsequence) for classification. In this study, we used two datasets: an open data set that is treated as a benchmark; the other dataset is our sleep stages dataset (available for download) to verify the results further. Our experiments showed that the combination of DBN with LSTM gives better overall accuracy 98.75\% (Fscore=0.9875) for benchmark dataset and 98.94\% (Fscore=0.9894) for MKG dataset. This result is better than the state of the art of sleep stages classification that was 91.31\%.

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)