human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep learning in bioinformatics"):
  Abstract: In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e., omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e., deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies.

Includes members:
  Member "brain tumor segmentation with deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "deep belief networks used on high resolution multichannel electroencephalography data for seizure detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on deep learning based brain computer interface recent advances and new frontiers")
  Member "deep motif visualizing genomic sequence classifications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "technical note on transcription factor motif discovery from importance scores tf modisco version 0 5 1 1")
  Member "deepmirgene deep neural network based precursor microrna prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "applications of deep learning and reinforcement learning to biological data")
  Member "comparative study of deep learning software frameworks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "high performance i o for large scale deep learning")
  Member "deeptarget end to end learning framework for microrna target prediction using deep recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepcci end to end deep learning for chemical chemical interaction prediction")
  Member "deepspark spark based deep learning supporting asynchronous updates and caffe compatibility":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep leakage from gradients")
  Member "deepspark a spark based distributed deep learning framework for commodity clusters":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep leakage from gradients")

Question: Please tell me which class the source paper 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)