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: "dna level splice junction prediction using deep recurrent neural networks"
  Abstract: A eukaryotic gene consists of multiple exons (protein coding regions) and introns (non-coding regions), and a splice junction refers to the boundary between a pair of exon and intron. Precise identification of spice junctions on a gene is important for deciphering its primary structure, function, and interaction. Experimental techniques for determining exon/intron boundaries include RNA-seq, which is often accompanied by computational approaches. Canonical splicing signals are known, but computational junction prediction still remains challenging because of a large number of false positives and other complications. In this paper, we exploit deep recurrent neural networks (RNNs) to model DNA sequences and to detect splice junctions thereon. We test various RNN units and architectures including long short-term memory units, gated recurrent units, and recently proposed iRNN for in-depth design space exploration. According to our experimental results, the proposed approach significantly outperforms not only conventional machine learning-based methods but also a recent state-of-the-art deep belief network-based technique in terms of prediction accuracy.

Connected through hyperedge HE-1 (induced by source paper "deepcci end to end deep learning for chemical chemical interaction prediction"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "generating focussed molecule libraries for drug discovery with recurrent neural networks"
    - "deepmirgene deep neural network based precursor microrna prediction"
    - "deeptarget end to end learning framework for microrna target prediction using deep recurrent neural networks"
    - "collaborative recurrent autoencoder recommend while learning to fill in the blanks"
    - "deep learning in bioinformatics"
    - "a critical review of recurrent neural networks for sequence learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (induced by source paper "dna steganalysis using deep recurrent neural networks"):
  with members:
    - "on the statistical detection of adversarial examples"
    - "deepmirgene deep neural network based precursor microrna prediction"
    - "using non invertible data transformations to build adversarial robust neural networks"
    - "deep learning in bioinformatics"
    - "learning the number of neurons in deep networks"
    - "unsupervised learning of video representations using lstms"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-3 (induced by source paper "deep learning in bioinformatics"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "deep belief networks used on high resolution multichannel electroencephalography data for seizure detection"
    - "deep motif visualizing genomic sequence classifications"
    - "deepmirgene deep neural network based precursor microrna prediction"
    - "comparative study of deep learning software frameworks"
    - "deeptarget end to end learning framework for microrna target prediction using deep recurrent neural networks"
    - "deepspark spark based deep learning supporting asynchronous updates and caffe compatibility"
    - "deepspark a spark based distributed deep learning framework for commodity clusters"

Connected through hyperedge HE-4 (induced by source paper "deeptarget end to end learning framework for microrna target prediction using deep recurrent neural networks"):
  with members:
    - "not just a black box learning important features through propagating activation differences"
    - "deep learning in bioinformatics"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "unsupervised learning of video representations using lstms"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

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)