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: "opennmt neural machine translation toolkit"
  Abstract: OpenNMT is an open-source toolkit for neural machine translation (NMT). The system prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research into model architectures, feature representations, and source modalities, while maintaining competitive performance and reasonable training requirements. The toolkit consists of modeling and translation support, as well as detailed pedagogical documentation about the underlying techniques. OpenNMT has been used in several production MT systems, modified for numerous research papers, and is implemented across several deep learning frameworks.

Connected through hyperedge HE-1 (induced by source paper "evaluating sequence to sequence learning models for if then program synthesis"):
  with members:
    - "joey nmt a minimalist nmt toolkit for novices"
    - "interactive semantic parsing for if then recipes via hierarchical reinforcement learning"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "attention is all you need"
    - "latent attention for if then program synthesis"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-2 (induced by source paper "distilling the knowledge of bert for text generation"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "towards making the most of bert in neural machine translation"
    - "mask predict parallel decoding of conditional masked language models"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "pretraining based natural language generation for text summarization"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "patient knowledge distillation for bert model compression"

Connected through hyperedge HE-3 (induced by source paper "on layer normalization in the transformer architecture"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "learning deep transformer models for machine translation"
    - "a mean field theory of batch normalization"
    - "on the variance of the adaptive learning rate and beyond"
    - "understanding and improving transformer from a multi particle dynamic system point of view"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fixup initialization residual learning without normalization"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-4 (induced by source paper "curate and generate a corpus and method for joint control of semantics and style in neural nlg"):
  with members:
    - "constrained decoding for neural nlg from compositional representations in task oriented dialogue"
    - "step by step separating planning from realization in neural data to text generation"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "characterizing variation in crowd sourced data for training neural language generators to produce stylistically varied outputs"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "toward controlled generation of text"

Connected through hyperedge HE-5 (induced by source paper "bootstrapping disjoint datasets for multilingual multimodal representation learning"):
  with members:
    - "lessons learned in multilingual grounded language learning"
    - "zero resource neural machine translation with multi agent communication game"
    - "imagination improves multimodal translation"
    - "learning visually grounded sentence representations"
    - "limitations of cross lingual learning from image search"
    - "vse improving visual semantic embeddings with hard negatives"
    - "emergent translation in multi agent communication"
    - "image pivoting for learning multilingual multimodal representations"

Connected through hyperedge HE-6 (induced by source paper "korean to chinese machine translation using chinese character as pivot clue"):
  with members:
    - "semantic role labeling with associated memory network"
    - "dependency or span end to end uniform semantic role labeling"
    - "lattice based transformer encoder for neural machine translation"
    - "finding better subword segmentation for neural machine translation"
    - "exploring recombination for efficient decoding of neural machine translation"
    - "attention is all you need"
    - "attention focusing for neural machine translation by bridging source and target embeddings"
    - "fast and accurate neural word segmentation for chinese"

Connected through hyperedge HE-7 (induced by source paper "improving scientific article visibility by neural title simplification"):
  with members:
    - "data driven summarization of scientific articles"
    - "dynamic multi level multi task learning for sentence simplification"
    - "sentence simplification with deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "joint copying and restricted generation for paraphrase"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "vizseq a visual analysis toolkit for text generation tasks"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "compare mt a tool for holistic comparison of language generation systems"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "cider consensus based image description evaluation"

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)