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: "multimodal compact bilinear pooling for multimodal neural machine translation"
  Abstract: In state-of-the-art Neural Machine Translation, an attention mechanism is used during decoding to enhance the translation. At every step, the decoder uses this mechanism to focus on different parts of the source sentence to gather the most useful information before outputting its target word. Recently, the effectiveness of the attention mechanism has also been explored for multimodal tasks, where it becomes possible to focus both on sentence parts and image regions. Approaches to pool two modalities usually include element-wise product, sum or concatenation. In this paper, we evaluate the more advanced Multimodal Compact Bilinear pooling method, which takes the outer product of two vectors to combine the attention features for the two modalities. This has been previously investigated for visual question answering. We try out this approach for multimodal image caption translation and show improvements compared to basic combination methods.

Connected through hyperedge HE-1 (induced by source paper "neuron interaction based representation composition for neural machine translation"):
  with members:
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "learning representations from imperfect time series data via tensor rank regularization"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "low rank tensor completion for multiway visual data"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "tensor regression networks"
    - "tensor fusion network for multimodal sentiment analysis"
    - "weighted low rank tensor recovery for hyperspectral image restoration"
    - "tensor product generation networks"

Connected through hyperedge HE-3 (induced by source paper "adversarial reconstruction for multi modal machine translation"):
  with members:
    - "lium cvc submissions for wmt18 multimodal translation task"
    - "unsupervised image captioning"
    - "spectral normalization for generative adversarial networks"
    - "umons submission for wmt18 multimodal translation task"
    - "imagination improves multimodal translation"
    - "modulating and attending the source image during encoding improves multimodal translation"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "generalized bilinear deep convolutional neural networks for multimodal biometric identification"):
  with members:
    - "attribute centered loss for soft biometrics guided face sketch photo recognition"
    - "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
    - "the nd iris 0405 iris image dataset"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "learning face representation from scratch"

Connected through hyperedge HE-5 (induced by source paper "visually grounded word embeddings and richer visual features for improving multimodal neural machine translation"):
  with members:
    - "doubly attentive decoder for multi modal neural machine translation"
    - "cuni system for wmt16 automatic post editing and multimodal translation tasks"
    - "does multimodality help human and machine for translation and image captioning"
    - "multi30k multilingual english german image descriptions"
    - "show attend and tell neural image caption generation with visual attention"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep residual learning for image recognition"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-6 (induced by source paper "cuni system for the wmt17 multimodal translation task"):
  with members:
    - "self critical sequence training for image captioning"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "improving neural machine translation models with monolingual data"
    - "show attend and tell neural image caption generation with visual attention"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"
    - "sequence level training with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "an empirical study on the effectiveness of images in multimodal neural machine translation"):
  with members:
    - "doubly attentive decoder for multi modal neural machine translation"
    - "multi30k multilingual english german image descriptions"
    - "effective approaches to attention based neural machine translation"
    - "deep residual learning for image recognition"
    - "neural machine translation of rare words with subword units"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"
    - "multiple object recognition with visual attention"

Connected through hyperedge HE-8 (induced by source paper "modulating and attending the source image during encoding improves multimodal translation"):
  with members:
    - "imagination improves multimodal translation"
    - "modulating early visual processing by language"
    - "an empirical study on the effectiveness of images in multimodal neural machine translation"
    - "multi30k multilingual english german image descriptions"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"

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)