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: "analyzing and exploiting narx recurrent neural networks for long term dependencies"
  Abstract: Recurrent neural networks (RNNs) have achieved state-of-the-art performance on many diverse tasks, from machine translation to surgical activity recognition, yet training RNNs to capture long-term dependencies remains difficult. To date, the vast majority of successful RNN architectures alleviate this problem using nearly-additive connections between states, as introduced by long short-term memory (LSTM). We take an orthogonal approach and introduce MIST RNNs, a NARX RNN architecture that allows direct connections from the very distant past. We show that MIST RNNs 1) exhibit superior vanishing-gradient properties in comparison to LSTM and previously-proposed NARX RNNs; 2) are far more efficient than previously-proposed NARX RNN architectures, requiring even fewer computations than LSTM; and 3) improve performance substantially over LSTM and Clockwork RNNs on tasks requiring very long-term dependencies.

Connected through hyperedge HE-1 (induced by source paper "learning longer term dependencies via grouped distributor unit"):
  with members:
    - "lstm a search space odyssey"
    - "dilated recurrent neural networks"
    - "minimal gated unit for recurrent neural networks"
    - "recurrent batch normalization"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "unitary evolution recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "multi variable lstm neural network for autoregressive exogenous model"):
  with members:
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "learning deep matrix representations"
    - "attention is all you need"
    - "factorization tricks for lstm networks"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "tensorizing neural networks"
    - "learning to diagnose with lstm recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "hierarchical attention based recurrent highway networks for time series prediction"):
  with members:
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "recurrent highway networks"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "learning representations from eeg with deep recurrent convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras"):
  with members:
    - "deep learning for generic object detection a survey"
    - "hierarchical attention based recurrent highway networks for time series prediction"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "flow guided feature aggregation for video object detection"
    - "underwater fish tracking for moving cameras based on deformable multiple kernels"
    - "deep feature flow for video recognition"
    - "you only look once unified real time object detection"

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)