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: "nmtpy a flexible toolkit for advanced neural machine translation systems"
  Abstract: In this paper, we present nmtpy, a flexible Python toolkit based on Theano for training Neu-ral Machine Translation and other neural sequence-to-sequence architectures. nmtpy decouples the specification of a network from the training and inference utilities to simplify the addition of a new architecture and reduce the amount of boilerplate code to be written. nmtpy has been used for LIUM's top-ranked submissions to WMT Multimodal Machine Translation and News Translation tasks in 2016 and 2017.

Connected through hyperedge HE-1 (induced by source paper "looking enhances listening recovering missing speech using images"):
  with members:
    - "how2 a large scale dataset for multimodal language understanding"
    - "visual features for context aware speech recognition"
    - "end to end attention based large vocabulary speech recognition"
    - "deep residual learning for image recognition"
    - "deep multimodal semantic embeddings for speech and images"

Connected through hyperedge HE-2 (induced by source paper "multimodal abstractive summarization for how2 videos"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "how2 a large scale dataset for multimodal language understanding"
    - "product title refinement via multi modal generative adversarial learning"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "get to the point summarization with pointer generator networks"
    - "attention strategies for multi source sequence to sequence learning"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-3 (induced by source paper "multitask learning for different subword segmentations in neural machine translation"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "optimizing segmentation granularity for neural machine translation"
    - "learning to segment inputs for nmt favors character level processing"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "improving character based decoding using target side morphological information for neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "attention is all you need"
    - "exploiting linguistic resources for neural machine translation using multi task learning"

Connected through hyperedge HE-4 (induced by source paper "debiasing word embeddings improves multimodal machine translation"):
  with members:
    - "multimodal machine translation with embedding prediction"
    - "lium cvc submissions for wmt18 multimodal translation task"
    - "cuni system for the wmt18 multimodal translation task"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "a visual attention grounding neural model for multimodal machine translation"
    - "imagination improves multimodal translation"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "all but the top simple and effective postprocessing for word representations"

Connected through hyperedge HE-5 (induced by source paper "probing the need for visual context in multimodal machine translation"):
  with members:
    - "lium cvc submissions for wmt18 multimodal translation task"
    - "cuni system for the wmt18 multimodal translation task"
    - "the memad submission to the wmt18 multimodal translation task"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "attention strategies for multi source sequence to sequence learning"
    - "osu multimodal machine translation system report"
    - "lium cvc submissions for wmt17 multimodal translation task"
    - "multimodal attention for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "analyzing utility of visual context in multimodal speech recognition under noisy conditions"):
  with members:
    - "probing the need for visual context in multimodal machine translation"
    - "modality attention for end to end audio visual speech recognition"
    - "how2 a large scale dataset for multimodal language understanding"
    - "visual features for context aware speech recognition"
    - "attention strategies for multi source sequence to sequence learning"
    - "lipnet end to end sentence level lipreading"
    - "lip reading sentences in the wild"
    - "using the output embedding to improve language models"

Connected through hyperedge HE-7 (induced by source paper "multimodal machine translation with embedding prediction"):
  with members:
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "cuni system for the wmt18 multimodal translation task"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "learning word vectors for 157 languages"
    - "imagination improves multimodal translation"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "all but the top simple and effective postprocessing for word representations"
    - "lium cvc submissions for wmt17 multimodal translation task"

Connected through hyperedge HE-8 (induced by source paper "findings of the second shared task on multimodal machine translation and multilingual image description"):
  with members:
    - "cross linguistic differences and similarities in image descriptions"
    - "imagination improves multimodal translation"
    - "osu multimodal machine translation system report"
    - "stair captions constructing a large scale japanese image caption dataset"
    - "unsupervised visual sense disambiguation for verbs using multimodal embeddings"
    - "multimodal pivots for image caption translation"
    - "multi30k multilingual english german image descriptions"
    - "edinburgh neural machine translation systems for wmt 16"

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)