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 echo state networks for diagnosis of parkinson s disease"
  Abstract: In this paper, we introduce a novel approach for diagnosis of Parkinson's Disease (PD) based on deep Echo State Networks (ESNs). The identification of PD is performed by analyzing the whole time-series collected from a tablet device during the sketching of spiral tests, without the need for feature extraction and data preprocessing. We evaluated the proposed approach on a public dataset of spiral tests. The results of experimental analysis show that DeepESNs perform significantly better than shallow ESN model. Overall, the proposed approach obtains state-of-the-art results in the identification of PD on this kind of temporal data.

Connected through hyperedge HE-1 (induced by source paper "bioinformatics and medicine in the era of deep learning"):
  with members:
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "capturing variabilities from computed tomography images with generative adversarial networks"
    - "learning compressed representations of blood samples time series with missing data"
    - "towards interpretable deep neural networks by leveraging adversarial examples"
    - "opening the black box of deep neural networks via information"
    - "a survey on deep learning in medical image analysis"
    - "learning convolutional neural networks for graphs"
    - "deep learning in bioinformatics"

Connected through hyperedge HE-2 (induced by source paper "deep echo state network deepesn a brief survey"):
  with members:
    - "short term memory of deep rnn"
    - "comparison between deepesns and gated rnns on multivariate time series prediction"
    - "hierarchical temporal representation in linear reservoir computing"
    - "how to construct deep recurrent neural networks"
    - "speech recognition with deep recurrent neural networks"

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)