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: "fine grained attention mechanism for neural machine translation"
  Abstract: Neural machine translation (NMT) has been a new paradigm in machine translation, and the attention mechanism has become the dominant approach with the state-of-the-art records in many language pairs. While there are variants of the attention mechanism, all of them use only temporal attention where one scalar value is assigned to one context vector corresponding to a source word. In this paper, we propose a fine-grained (or 2D) attention mechanism where each dimension of a context vector will receive a separate attention score. In experiments with the task of En-De and En-Fi translation, the fine-grained attention method improves the translation quality in terms of BLEU score. In addition, our alignment analysis reveals how the fine-grained attention mechanism exploits the internal structure of context vectors.

Connected through hyperedge HE-1 (induced by source paper "exploring interpretable lstm neural networks over multi variable data"):
  with members:
    - "an interpretable lstm neural network for autoregressive exogenous model"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "deep multi output forecasting learning to accurately predict blood glucose trajectories"
    - "methods for interpreting and understanding deep neural networks"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "a dual stage attention based recurrent neural network for time series prediction"

Connected through hyperedge HE-2 (induced by source paper "signed distance based deep memory recommender"):
  with members:
    - "regularizing matrix factorization with user and item embeddings for recommendation"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "collaborative memory network for recommendation systems"
    - "gated attentive autoencoder for content aware recommendation"
    - "point of interest recommendation exploiting self attentive autoencoders with neighbor aware influence"
    - "multi pointer co attention networks for recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "object based multi temporal and multi source land cover mapping leveraging hierarchical class relationships"):
  with members:
    - "temporal convolutional neural network for the classification of satellite image time series"
    - "duplo a dual view point deep learning architecture for time series classification"
    - "attention is all you need"
    - "land cover classification via multitemporal spatial data by deep recurrent neural networks"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "efficient attention using a fixed size memory representation"
    - "why should i trust you explaining the predictions of any classifier"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "unsupervised network embedding for graph visualization clustering and classification"):
  with members:
    - "detecting sequences of system states in temporal networks"
    - "multi hop assortativities for network classification"
    - "graph embedding techniques applications and performance a survey"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "feature based classification of networks"
    - "detecting change points in the large scale structure of evolving networks"

Connected through hyperedge HE-5 (induced by source paper "enhancing sentence embedding with generalized pooling"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "neural natural language inference models enhanced with external knowledge"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "a compare propagate architecture with alignment factorization for natural language inference"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "exploring question understanding and adaptation in neural network based question answering"

Connected through hyperedge HE-6 (induced by source paper "persistent hidden states and nonlinear transformation for long short term memory"):
  with members:
    - "lstm a search space odyssey"
    - "associative long short term memory"
    - "context dependent word representation for neural machine translation"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "pointer sentinel mixture models"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "unitary evolution recurrent neural networks"
    - "neural machine translation of rare words with subword units"

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.CL(Computation and Language)