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: "neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy"
  Abstract: Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph - a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 instead of 30 second scoring epochs. A T1N marker based on unusual sleep-stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.

Connected through hyperedge HE-1 (induced by source paper "seqsleepnet end to end hierarchical recurrent neural network for sequence to sequence automatic sleep staging"):
  with members:
    - "joint classification and prediction cnn framework for automatic sleep stage classification"
    - "detection of rem sleep behaviour disorder by automated polysomnography analysis"
    - "audio scene classification with deep recurrent neural networks"
    - "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-2 (induced by source paper "intra and inter epoch temporal context network iitnet for automatic sleep stage scoring"):
  with members:
    - "joint classification and prediction cnn framework for automatic sleep stage classification"
    - "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"
    - "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-3 (induced by source paper "joint classification and prediction cnn framework for automatic sleep stage classification"):
  with members:
    - "audio scene classification with deep recurrent neural networks"
    - "robust audio event recognition with 1 max pooling convolutional neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

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.NE(Neural and Evolutionary Computing)