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: "word ordering without syntax"
  Abstract: Recent work on word ordering has argued that syntactic structure is important, or even required, for effectively recovering the order of a sentence. We find that, in fact, an n-gram language model with a simple heuristic gives strong results on this task. Furthermore, we show that a long short-term memory (LSTM) language model is even more effective at recovering order, with our basic model outperforming a state-of-the-art syntactic model by 11.5 BLEU points. Additional data and larger beams yield further gains, at the expense of training and search time.

Connected through hyperedge HE-1 (induced by source paper "non monotonic sequential text generation"):
  with members:
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "blockwise parallel decoding for deep autoregressive models"
    - "relational inductive biases deep learning and graph networks"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "texygen a benchmarking platform for text generation models"
    - "language gans falling short"
    - "the importance of generation order in language modeling"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-2 (induced by source paper "structbert incorporating language structures into pre training for deep language understanding"):
  with members:
    - "spanbert improving pre training by representing and predicting spans"
    - "multi task deep neural networks for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "creative gans for generating poems lyrics and metaphors"):
  with members:
    - "the curious case of neural text degeneration"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fine tuned language models for text classification"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "a deep reinforced model for abstractive summarization"
    - "chinese song iambics generation with neural attention based model"

Connected through hyperedge HE-4 (induced by source paper "neural transition based syntactic linearization"):
  with members:
    - "graph to sequence learning using gated graph neural networks"
    - "attention is all you need"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "semi supervised classification with graph convolutional networks"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "recurrent neural network regularization"

Connected through hyperedge HE-5 (induced by source paper "obj2text generating visually descriptive language from object layouts"):
  with members:
    - "who is mistaken"
    - "image captioning with semantic attention"
    - "yolo9000 better faster stronger"
    - "grounding of textual phrases in images by reconstruction"
    - "boosting image captioning with attributes"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "natural language object retrieval"

Connected through hyperedge HE-6 (induced by source paper "style transfer from non parallel text by cross alignment"):
  with members:
    - "toward controlled generation of text"
    - "opennmt open source toolkit for neural machine translation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "maximum likelihood augmented discrete generative adversarial networks"
    - "differentiable scheduled sampling for credit assignment"
    - "dualgan unsupervised dual learning for image to image translation"

Connected through hyperedge HE-7 (induced by source paper "a comparison of neural models for word ordering"):
  with members:
    - "edinburgh neural machine translation systems for wmt 16"
    - "show attend and tell neural image caption generation with visual attention"
    - "neural machine translation by jointly learning to align and translate"
    - "recurrent neural network regularization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "sequence to sequence learning as beam search optimization"):
  with members:
    - "globally normalized transition based neural networks"
    - "an actor critic algorithm for sequence prediction"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "minimum risk training for neural machine translation"
    - "sequence level training with recurrent neural networks"
    - "sequence to sequence video to text"

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)