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: "deep motif visualizing genomic sequence classifications"
  Abstract: This paper applies a deep convolutional/highway MLP framework to classify genomic sequences on the transcription factor binding site task. To make the model understandable, we propose an optimization driven strategy to extract "motifs", or symbolic patterns which visualize the positive class learned by the network. We show that our system, Deep Motif (DeMo), extracts motifs that are similar to, and in some cases outperform the current well known motifs. In addition, we find that a deeper model consisting of multiple convolutional and highway layers can outperform a single convolutional and fully connected layer in the previous state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "technical note on transcription factor motif discovery from importance scores tf modisco version 0 5 1 1"):
  with members:
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-2 (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"
    - "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"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"

Connected through hyperedge HE-3 (induced by source paper "genetic architect discovering genomic structure with learned neural architectures"):
  with members:
    - "deep residual networks with exponential linear unit"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "training very deep networks"
    - "adam a method for stochastic optimization"
    - "striving for simplicity the all convolutional net"
    - "spatially sparse convolutional neural networks"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "deepchrome deep learning for predicting gene expression from histone modifications"):
  with members:
    - "must cnn a multilayer shift and stitch deep convolutional architecture for sequence based protein structure prediction"
    - "understanding neural networks through deep visualization"
    - "going deeper with convolutions"
    - "convolutional neural networks for sentence classification"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "recurrent convolutional neural networks for scene parsing"

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)