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 recurrent neural networks for sequential phenotype prediction in genomics"
  Abstract: In analyzing of modern biological data, we are often dealing with ill-posed problems and missing data, mostly due to high dimensionality and multicollinearity of the dataset. In this paper, we have proposed a system based on matrix factorization (MF) and deep recurrent neural networks (DRNNs) for genotype imputation and phenotype sequences prediction. In order to model the long-term dependencies of phenotype data, the new Recurrent Linear Units (ReLU) learning strategy is utilized for the first time. The proposed model is implemented for parallel processing on central processing units (CPUs) and graphic processing units (GPUs). Performance of the proposed model is compared with other training algorithms for learning long-term dependencies as well as the sparse partial least square (SPLS) method on a set of genotype and phenotype data with 604 samples, 1980 single-nucleotide polymorphisms (SNPs), and two traits. The results demonstrate performance of the ReLU training algorithm in learning long-term dependencies in RNNs.

Connected through hyperedge HE-1 (induced by source paper "cylindrical transform 3d semantic segmentation of kidneys with limited annotated images"):
  with members:
    - "recent advances in recurrent neural networks"
    - "an analysis of deep neural network models for practical applications"
    - "generalization of deep neural networks for chest pathology classification in x rays using generative adversarial networks"
    - "image augmentation using radial transform for training deep neural networks"
    - "interpretation of mammogram and chest radiograph reports using deep neural networks preliminary results"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "a convolutional neural network for search term detection"):
  with members:
    - "learning over long time lags"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "a convolutional neural network for modelling sentences"
    - "speech recognition with deep recurrent neural networks"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "generalization of deep neural networks for chest pathology classification in x rays using generative adversarial networks"):
  with members:
    - "learning representations of emotional speech with deep convolutional generative adversarial networks"
    - "image augmentation using radial transform for training deep neural networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "image augmentation using radial transform for training deep neural networks"):
  with members:
    - "recent advances in recurrent neural networks"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (induced by source paper "a conceptual development of quench prediction app build on lstm and elqa framework"):
  with members:
    - "lstm a search space odyssey"
    - "a critical review of recurrent neural networks for sequence learning"
    - "the numpy array a structure for efficient numerical computation"

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)