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: "deepmirgene deep neural network based precursor microrna prediction"
  Abstract: Since microRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation, miRNA identification is one of the most essential problems in computational biology. miRNAs are usually short in length ranging between 20 and 23 base pairs. It is thus often difficult to distinguish miRNA-encoding sequences from other non-coding RNAs and pseudo miRNAs that have a similar length, and most previous studies have recommended using precursor miRNAs instead of mature miRNAs for robust detection. A great number of conventional machine-learning-based classification methods have been proposed, but they often have the serious disadvantage of requiring manual feature engineering, and their performance is limited as well. In this paper, we propose a novel miRNA precursor prediction algorithm, deepMiRGene, based on recurrent neural networks, specifically long short-term memory networks. deepMiRGene automatically learns suitable features from the data themselves without manual feature engineering and constructs a model that can successfully reflect structural characteristics of precursor miRNAs. For the performance evaluation of our approach, we have employed several widely used evaluation metrics on three recent benchmark datasets and verified that deepMiRGene delivered comparable performance among the current state-of-the-art tools.

Connected through hyperedge HE-1 (induced by source paper "applications of deep learning and reinforcement learning to biological data"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "brain tumor segmentation with deep neural networks"
    - "deep reinforcement learning an overview"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deep learning trends for focal brain pathology segmentation in mri"
    - "medical image denoising using convolutional denoising autoencoders"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "sample efficient actor critic with experience replay"

Connected through hyperedge HE-2 (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"
    - "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"
    - "dna level splice junction prediction using deep recurrent neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "dna steganalysis using deep recurrent neural networks"):
  with members:
    - "on the statistical detection of adversarial examples"
    - "using non invertible data transformations to build adversarial robust neural networks"
    - "deep learning in bioinformatics"
    - "learning the number of neurons in deep networks"
    - "dna level splice junction prediction using deep recurrent neural 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-4 (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"
    - "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"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"

Connected through hyperedge HE-5 (induced by source paper "an efficient approach to boosting performance of deep spiking network training"):
  with members:
    - "deep spiking networks"
    - "deeptarget end to end learning framework for microrna target prediction using deep recurrent neural networks"
    - "deep learning in bioinformatics"
    - "training deep spiking neural networks using backpropagation"
    - "neural machine translation by jointly learning to align and translate"
    - "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)