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: "globally normalized transition based neural networks"
  Abstract: We introduce a globally normalized transition-based neural network model that achieves state-of-the-art part-of-speech tagging, dependency parsing and sentence compression results. Our model is a simple feed-forward neural network that operates on a task-specific transition system, yet achieves comparable or better accuracies than recurrent models. We discuss the importance of global as opposed to local normalization: a key insight is that the label bias problem implies that globally normalized models can be strictly more expressive than locally normalized models.

Connected through hyperedge HE-1 (induced by source paper "consistency of a recurrent language model with respect to incomplete decoding"):
  with members:
    - "the curious case of neural text degeneration"
    - "neural text generation with unlikelihood training"
    - "on nmt search errors and model errors cat got your tongue"
    - "hierarchical neural story generation"
    - "correcting length bias in neural machine translation"
    - "on the state of the art of evaluation in neural language models"
    - "recurrent neural networks as weighted language recognizers"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "is pos tagging necessary or even helpful for neural dependency parsing"):
  with members:
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "75 languages 1 model parsing universal dependencies universally"
    - "hearst patterns revisited automatic hypernym detection from large text corpora"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "constituency parsing with a self attentive encoder"
    - "deep biaffine attention for neural dependency parsing"

Connected through hyperedge HE-3 (induced by source paper "towards quantifying the distance between opinions"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "viewpoint discovery and understanding in social networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "distributed representations of sentences and documents"
    - "opinion mining and analysis a survey"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "generalization in generation a closer look at exposure bias"):
  with members:
    - "deep reinforcement learning with distributional semantic rewards for abstractive summarization"
    - "quantifying exposure bias for neural language generation"
    - "sample efficient text summarization using a single pre trained transformer"
    - "autoregressive text generation beyond feedback loops"
    - "deep reinforcement learning for sequence to sequence models"
    - "neural text generation past present and beyond"
    - "learning actionable representations with goal conditioned policies"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-5 (induced by source paper "autoregressive text generation beyond feedback loops"):
  with members:
    - "on accurate evaluation of gans for language generation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "large scale classification of structured objects using a crf with deep class embedding"
    - "deep state space models for unconditional word generation"
    - "language gans falling short"
    - "dp gan diversity promoting generative adversarial network for generating informative and diversified text"
    - "structured attention networks"
    - "advances in variational inference"

Connected through hyperedge HE-6 (induced by source paper "head driven phrase structure grammar parsing on penn treebank"):
  with members:
    - "lattice based transformer encoder for neural machine translation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what s going on in neural constituency parsers an analysis"
    - "two local models for neural constituent parsing"
    - "exploring recombination for efficient decoding of neural machine translation"
    - "multilingual constituency parsing with self attention and pre training"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"

Connected through hyperedge HE-7 (induced by source paper "on the downstream performance of compressed word embeddings"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "tensorized embedding layers for efficient model compression"
    - "low memory neural network training a technical report"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "the state of sparsity in deep neural networks"
    - "high accuracy low precision training"
    - "low precision random fourier features for memory constrained kernel approximation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-8 (induced by source paper "things you may not know about adversarial example a black box adversarial image attack"):
  with members:
    - "spatially transformed adversarial examples"
    - "formal security analysis of neural networks using symbolic intervals"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "testing deep neural networks"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "adversarial examples are not easily detected bypassing ten detection methods"

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)