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: "non monotonic sequential text generation"
  Abstract: Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation. Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree. Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy's own preferences. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order, while achieving competitive performance with conventional left-to-right generation.

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 "insertion deletion transformer"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "levenshtein transformer"
    - "kermit generative insertion based modeling for sequences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "optimal completion distillation for sequence learning"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "generaci on autom atica de frases literarias en espa nol"):
  with members:
    - "neural text generation from structured data with application to the biography domain"
    - "beyond stemming and lemmatization ultra stemming to improve automatic text summarization"

Connected through hyperedge HE-4 (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"
    - "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"
    - "blockwise parallel decoding for deep autoregressive models"

Connected through hyperedge HE-5 (induced by source paper "how decoding strategies affect the verifiability of generated text"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "language models as knowledge bases"
    - "investigating entity knowledge in bert with simple neural end to end entity linking"
    - "mixture content selection for diverse sequence generation"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "the curious case of neural text degeneration"
    - "tmlab generative enhanced model gem for adversarial attacks"
    - "real or fake learning to discriminate machine from human generated text"

Connected through hyperedge HE-6 (induced by source paper "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "preventing posterior collapse with delta vaes"
    - "flowseq non autoregressive conditional sequence generation with generative flow"
    - "image transformer"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "auto encoding variational neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "fine tuning by curriculum learning for non autoregressive neural machine translation"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "hint based training for non autoregressive machine translation"
    - "on the power of curriculum learning in training deep networks"
    - "fastspeech fast robust and controllable text to speech"
    - "mass masked sequence to sequence pre training for language generation"
    - "achieving human parity on automatic chinese to english news translation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"

Connected through hyperedge HE-8 (induced by source paper "sequence modeling with unconstrained generation order"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "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"
    - "a call for clarity in reporting bleu scores"

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)