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: "dynamic layer aggregation for neural machine translation with routing by agreement"
  Abstract: With the promising progress of deep neural networks, layer aggregation has been used to fuse information across layers in various fields, such as computer vision and machine translation. However, most of the previous methods combine layers in a static fashion in that their aggregation strategy is independent of specific hidden states. Inspired by recent progress on capsule networks, in this paper we propose to use routing-by-agreement strategies to aggregate layers dynamically. Specifically, the algorithm learns the probability of a part (individual layer representations) assigned to a whole (aggregated representations) in an iterative way and combines parts accordingly. We implement our algorithm on top of the state-of-the-art neural machine translation model TRANSFORMER and conduct experiments on the widely-used WMT14 English-German and WMT17 Chinese-English translation datasets. Experimental results across language pairs show that the proposed approach consistently outperforms the strong baseline model and a representative static aggregation model.

Connected through hyperedge HE-1 (induced by source paper "dynamic past and future for neural machine translation"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "information aggregation for multi head attention with routing by agreement"
    - "neural machine translation with adequacy oriented learning"
    - "deconvolution based global decoding for neural machine translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "investigating capsule networks with dynamic routing for text classification"
    - "information aggregation via dynamic routing for sequence encoding"
    - "a hybrid word character model for abstractive summarization"

Connected through hyperedge HE-2 (induced by source paper "atloc attention guided camera localization"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "local supports global deep camera relocalization with sequence enhancement"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "context aware self attention networks"
    - "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-3 (induced by source paper "exploiting sentential context for neural machine translation"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "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"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-4 (induced by source paper "neuron interaction based representation composition for neural machine translation"):
  with members:
    - "information aggregation for multi head attention with routing by agreement"
    - "exploiting deep representations for neural machine translation"
    - "weighted transformer network for machine translation"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi head attention with disagreement regularization"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-5 (induced by source paper "multi perspective inferrer reasoning sentences relationship from holistic perspective"):
  with members:
    - "dynamic past and future for neural machine translation"
    - "multi labeled relation extraction with attentive capsule network"
    - "hypothesis only baselines in natural language inference"
    - "information aggregation via dynamic routing for sequence encoding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "stochastic answer networks for natural language inference"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"

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)