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: "insertion based decoding with automatically inferred generation order"
  Abstract: Conventional neural autoregressive decoding commonly assumes a fixed left-to-right generation order, which may be sub-optimal. In this work, we propose a novel decoding algorithm -- InDIGO -- which supports flexible sequence generation in arbitrary orders through insertion operations. We extend Transformer, a state-of-the-art sequence generation model, to efficiently implement the proposed approach, enabling it to be trained with either a pre-defined generation order or adaptive orders obtained from beam-search. Experiments on four real-world tasks, including word order recovery, machine translation, image caption and code generation, demonstrate that our algorithm can generate sequences following arbitrary orders, while achieving competitive or even better performance compared to the conventional left-to-right generation. The generated sequences show that InDIGO adopts adaptive generation orders based on input information.

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 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"
    - "non monotonic sequential text generation"

Connected through hyperedge HE-2 (induced by source paper "semi autoregressive training improves mask predict decoding"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "pay less attention with lightweight and dynamic convolutions"
    - "understanding knowledge distillation in non autoregressive machine translation"
    - "mask predict parallel decoding of conditional masked language models"
    - "fast structured decoding for sequence models"
    - "levenshtein transformer"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"

Connected through hyperedge HE-3 (induced by source paper "insertion deletion transformer"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "non monotonic sequential text generation"
    - "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-4 (induced by source paper "real or fake learning to discriminate machine from human generated text"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "implicit generation and generalization in energy based models"
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"
    - "roberta a robustly optimized bert pretraining approach"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "blockwise parallel decoding for deep autoregressive models"

Connected through hyperedge HE-5 (induced by source paper "defending against neural fake news"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "the curious case of neural text degeneration"
    - "unifying human and statistical evaluation for natural language generation"
    - "mask predict parallel decoding of conditional masked language models"
    - "release strategies and the social impacts of language models"
    - "unsupervised domain adaptation of contextualized embeddings for sequence labeling"
    - "from recognition to cognition visual commonsense reasoning"

Connected through hyperedge HE-6 (induced by source paper "strategies for structuring story generation"):
  with members:
    - "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"
    - "hierarchical neural story generation"

Connected through hyperedge HE-7 (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 transformer flexible sequence generation via insertion operations"
    - "preventing posterior collapse with delta vaes"
    - "non monotonic sequential text generation"
    - "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-8 (induced by source paper "flowseq non autoregressive conditional sequence generation with generative flow"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "macow masked convolutional generative flow"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "mask predict parallel decoding of conditional masked language models"
    - "mixture models for diverse machine translation tricks of the trade"
    - "on the convergence of adam and beyond"

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)