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: "the importance of generation order in language modeling"
  Abstract: Neural language models are a critical component of state-of-the-art systems for machine translation, summarization, audio transcription, and other tasks. These language models are almost universally autoregressive in nature, generating sentences one token at a time from left to right. This paper studies the influence of token generation order on model quality via a novel two-pass language model that produces partially-filled sentence "templates" and then fills in missing tokens. We compare various strategies for structuring these two passes and observe a surprisingly large variation in model quality. We find the most effective strategy generates function words in the first pass followed by content words in the second. We believe these experimental results justify a more extensive investigation of generation order for neural language models.

Connected through hyperedge HE-1 (induced by source paper "blank language models"):
  with members:
    - "tigs an inference algorithm for text infilling with gradient search"
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "synchronous bidirectional neural machine translation"
    - "restoring ancient text using deep learning a case study on greek epigraphy"
    - "mask predict parallel decoding of conditional masked language models"
    - "ctrl a conditional transformer language model for controllable generation"
    - "transformer xl attentive language models beyond a fixed length context"

Connected through hyperedge HE-2 (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"
    - "fast policy learning through imitation and reinforcement"
    - "learning to parse and translate improves neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "insertion based decoding with automatically inferred generation order"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "synchronous bidirectional neural machine translation"
    - "sequence generation from both sides to the middle"
    - "non monotonic sequential text generation"
    - "text infilling"
    - "self attention with relative position representations"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "a tree based decoder for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "strategies for structuring story generation"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "generating wikipedia by summarizing long sequences"
    - "a skeleton based model for promoting coherence among sentences in narrative story generation"
    - "higher order coreference resolution with coarse to fine inference"
    - "building language models for text with named entities"

Connected through hyperedge HE-5 (induced by source paper "sequence modeling with unconstrained generation order"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "non monotonic sequential text generation"
    - "universal transformers"
    - "middle out decoding"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "beyond error propagation in neural machine translation characteristics of language also matter"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "discrete flows invertible generative models of discrete data"):
  with members:
    - "integer discrete flows and lossless compression"
    - "waveglow a flow based generative network for speech synthesis"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "blockwise parallel decoding for deep autoregressive models"
    - "bayesian layers a module for neural network uncertainty"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "sequential graph dependency parser"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "unsupervised recurrent neural network grammars"
    - "universal dependency parsing from scratch"
    - "left to right dependency parsing with pointer networks"
    - "non monotonic sequential text generation"
    - "learning deep generative models of graphs"
    - "saliency based sequential image attention with multiset prediction"

Connected through hyperedge HE-8 (induced by source paper "does order matter an empirical study on generating multiple keyphrases as a sequence"):
  with members:
    - "keyphrase generation a text summarization struggle"
    - "divgraphpointer a graph pointer network for extracting diverse keyphrases"
    - "keyphrase generation with correlation constraints"
    - "bottom up abstractive summarization"
    - "one size does not fit all generating and evaluating variable number of keyphrases"
    - "semi supervised learning for neural keyphrase generation"
    - "neural models for key phrase detection and question generation"
    - "deep keyphrase generation"

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.LG(Machine Learning)