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: "encoding source language with convolutional neural network for machine translation"
  Abstract: The recently proposed neural network joint model (NNJM) (Devlin et al., 2014) augments the n-gram target language model with a heuristically chosen source context window, achieving state-of-the-art performance in SMT. In this paper, we give a more systematic treatment by summarizing the relevant source information through a convolutional architecture guided by the target information. With different guiding signals during decoding, our specifically designed convolution+gating architectures can pinpoint the parts of a source sentence that are relevant to predicting a target word, and fuse them with the context of entire source sentence to form a unified representation. This representation, together with target language words, are fed to a deep neural network (DNN) to form a stronger NNJM. Experiments on two NIST Chinese-English translation tasks show that the proposed model can achieve significant improvements over the previous NNJM by up to +1.08 BLEU points on average

Connected through hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis"):
  with members:
    - "session based recommendation with graph neural network"
    - "contextual hybrid session based news recommendation with recurrent neural networks"
    - "on the importance of news content representation in hybrid neural session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "multi pointer co attention networks for recommendation"

Connected through hyperedge HE-2 (induced by source paper "temporal deformable convolutional encoder decoder networks for video captioning"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "jointly localizing and describing events for dense video captioning"
    - "exploring visual relationship for image captioning"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "integrating both visual and audio cues for enhanced video caption"
    - "convolutional sequence to sequence learning"
    - "hierarchical boundary aware neural encoder for video captioning"

Connected through hyperedge HE-3 (induced by source paper "exploiting sentential context for neural machine translation"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "information aggregation for multi head attention with routing by agreement"
    - "context aware self attention networks"
    - "universal transformers"
    - "modeling recurrence for transformer"
    - "deconvolution based global decoding for neural machine translation"
    - "exploiting deep representations for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "a sustainable multi modal multi layer emotion aware service at the edge"):
  with members:
    - "label less learning for traffic control in an edge network"
    - "a dynamic service migration mechanism in edge cognitive computing"
    - "wearable affective robot"
    - "energy costs for traffic offloading by cache enabled d2d communications"
    - "efficient multi user computation offloading for mobile edge cloud computing"

Connected through hyperedge HE-5 (induced by source paper "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"):
  with members:
    - "towards two dimensional sequence to sequence model in neural machine translation"
    - "weaver deep co encoding of questions and documents for machine reading"
    - "adversarial neural machine translation"
    - "towards neural phrase based machine translation"
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "table to text describing table region with natural language"):
  with members:
    - "challenges in data to document generation"
    - "pointing the unknown words"
    - "deep reinforcement learning for dialogue generation"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "interactive attention for neural machine translation"
    - "neural text generation from structured data with application to the biography domain"
    - "modeling coverage for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "text classification based on multiple block convolutional highways"):
  with members:
    - "semantics derived automatically from language corpora contain human like biases"
    - "very deep convolutional networks for end to end speech recognition"
    - "very deep convolutional networks for text classification"
    - "language modeling with gated convolutional networks"
    - "recurrent highway networks"
    - "simple question answering by attentive convolutional neural network"
    - "a convolutional encoder model for neural machine translation"
    - "deeptrack learning discriminative feature representations online for robust visual tracking"

Connected through hyperedge HE-8 (induced by source paper "fast decoding in sequence models using discrete latent variables"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "learning the base distribution in implicit generative models"
    - "discrete autoencoders for sequence models"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "attention is all you need"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "convolutional sequence to sequence learning"

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)