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: "joint classification and prediction cnn framework for automatic sleep stage classification"
  Abstract: Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This paper proposes a joint classification-and-prediction framework based on convolutional neural networks (CNNs) for automatic sleep staging, and, subsequently, introduces a simple yet efficient CNN architecture to power the framework. Given a single input epoch, the novel framework jointly determines its label (classification) and its neighboring epochs’ labels (prediction) in the contextual output. While the proposed framework is orthogonal to the widely adopted classification schemes, which take one or multiple epochs as contextual inputs and produce a single classification decision on the target epoch, we demonstrate its advantages in several ways. First, it leverages the dependency among consecutive sleep epochs while surpassing the problems experienced with the common classification schemes. Second, even with a single model, the framework has the capacity to produce multiple decisions, which are essential in obtaining a good performance as in ensemble-of-models methods, with very little induced computational overhead. Probabilistic aggregation techniques are then proposed to leverage the availability of multiple decisions. To illustrate the efficacy of the proposed framework, we conducted experiments on two public datasets: Sleep-EDF Expanded (Sleep-EDF), which consists of 20 subjects, and Montreal Archive of Sleep Studies (MASS) dataset, which consists of 200 subjects. The proposed framework yields an overall classification accuracy of 82.3% and 83.6%, respectively. We also show that the proposed framework not only is superior to the baselines based on the common classification schemes but also outperforms existing deep-learning approaches. To our knowledge, this is the first work going beyond the standard single-output classification to consider multitask neural networks for automatic sleep staging. This framework provides avenues for further studies of different neural-network architectures for automatic sleep staging.

Connected through hyperedge HE-1 (induced by source paper "u time a fully convolutional network for time series segmentation applied to sleep staging"):
  with members:
    - "one network to segment them all a general lightweight system for accurate 3d medical image segmentation"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "deep convolutional neural networks for interpretable analysis of eeg sleep stage scoring"
    - "attention is all you need"
    - "sleepnet automated sleep staging system via deep learning"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "cnn is all you need"

Connected through hyperedge HE-2 (induced by source paper "seqsleepnet end to end hierarchical recurrent neural network for sequence to sequence automatic sleep staging"):
  with members:
    - "detection of rem sleep behaviour disorder by automated polysomnography analysis"
    - "audio scene classification with deep recurrent neural networks"
    - "neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy"
    - "end to end learning for self driving cars"
    - "recurrent batch normalization"
    - "effective approaches to attention based neural machine translation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-3 (induced by source paper "intra and inter epoch temporal context network iitnet for automatic sleep stage scoring"):
  with members:
    - "seqsleepnet end to end hierarchical recurrent neural network for sequence to sequence automatic sleep staging"
    - "deep convolutional neural networks for interpretable analysis of eeg sleep stage scoring"
    - "sleepnet automated sleep staging system via deep learning"
    - "neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy"
    - "mask r cnn"
    - "identity mappings in deep residual networks"
    - "hierarchical question image co attention for visual question answering"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"

Connected through hyperedge HE-4 (induced by source paper "towards more accurate automatic sleep staging via deep transfer learning"):
  with members:
    - "seqsleepnet end to end hierarchical recurrent neural network for sequence to sequence automatic sleep staging"
    - "deep transfer learning for single channel automatic sleep staging with channel mismatch"
    - "detection of rem sleep behaviour disorder by automated polysomnography analysis"
    - "recurrent batch normalization"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-5 (induced by source paper "deep transfer learning for single channel automatic sleep staging with channel mismatch"):
  with members:
    - "seqsleepnet end to end hierarchical recurrent neural network for sequence to sequence automatic sleep staging"
    - "detection of rem sleep behaviour disorder by automated polysomnography analysis"
    - "effective approaches to attention based neural machine translation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)