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: "context aware self attention networks"
  Abstract: Self-attention model have shown its flexibility in parallel computation and the effectiveness on modeling both long- and short-term dependencies. However, it calculates the dependencies between representations without considering the contextual information, which have proven useful for modeling dependencies among neural representations in various natural language tasks. In this work, we focus on improving self-attention networks through capturing the richness of context. To maintain the simplicity and flexibility of the self-attention networks, we propose to contextualize the transformations of the query and key layers, which are used to calculates the relevance between elements. Specifically, we leverage the internal representations that embed both global and deep contexts, thus avoid relying on external resources. Experimental results on WMT14 English-German and WMT17 Chinese-English translation tasks demonstrate the effectiveness and universality of the proposed methods. Furthermore, we conducted extensive analyses to quantity how the context vectors participate in the self-attention model.

Connected through hyperedge HE-1 (induced by source paper "normalized and geometry aware self attention network for image captioning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "image captioning transforming objects into words"
    - "attention on attention for image captioning"
    - "vatex a large scale high quality multilingual dataset for video and language research"
    - "multimodal transformer with multi view visual representation for image captioning"
    - "large scale long tailed recognition in an open world"
    - "aligning linguistic words and visual semantic units for image captioning"
    - "deep modular co attention networks for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "gret global representation enhanced transformer"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "dynamic past and future for neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "deconvolution based global decoding for neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "global encoding for abstractive summarization"
    - "investigating capsule networks with dynamic routing for text classification"
    - "self attention with relative position representations"

Connected through hyperedge HE-3 (induced by source paper "two headed monster and crossed co attention networks"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "the evolved transformer"
    - "modeling recurrence for transformer"
    - "adaptive attention span in transformers"
    - "scaling neural machine translation"
    - "modeling localness for self attention networks"
    - "multi pointer co attention networks for recommendation"

Connected through hyperedge HE-4 (induced by source paper "dynamic layer aggregation for neural machine translation with routing by agreement"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "neural machine translation with adequacy oriented learning"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"
    - "information aggregation via dynamic routing for sequence encoding"

Connected through hyperedge HE-5 (induced by source paper "atloc attention guided camera localization"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "local supports global deep camera relocalization with sequence enhancement"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "image transformer"
    - "deep auxiliary learning for visual localization and odometry"
    - "global pose estimation with an attention based recurrent network"
    - "to find where you talk temporal sentence localization in video with attention based location regression"

Connected through hyperedge HE-6 (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"
    - "universal transformers"
    - "modeling recurrence for transformer"
    - "deconvolution based global decoding for neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-7 (induced by source paper "modeling recurrence for transformer"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "universal transformers"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "context aware neural machine translation learns anaphora resolution"
    - "ordered neurons integrating tree structures into recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "improving neural machine translation with parent scaled self attention"):
  with members:
    - "modeling latent sentence structure in neural machine translation"
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "inducing grammars with and for neural machine translation"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "modeling localness for self attention networks"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "linguistically informed self attention for semantic role labeling"

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)