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: "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
  Abstract: Although neural machine translation models reached high translation quality, the autoregressive nature makes inference difficult to parallelize and leads to high translation latency. Inspired by recent refinement-based approaches, we propose LaNMT, a latent-variable non-autoregressive model with continuous latent variables and deterministic inference procedure. In contrast to existing approaches, we use a deterministic inference algorithm to find the target sequence that maximizes the lowerbound to the log-probability. During inference, the length of translation automatically adapts itself. Our experiments show that the lowerbound can be greatly increased by running the inference algorithm, resulting in significantly improved translation quality. Our proposed model closes the performance gap between non-autoregressive and autoregressive approaches on ASPEC Ja-En dataset with 8.6x faster decoding. On WMT'14 En-De dataset, our model narrows the gap with autoregressive baseline to 2.0 BLEU points with 12.5x speedup. By decoding multiple initial latent variables in parallel and rescore using a teacher model, the proposed model further brings the gap down to 1.0 BLEU point on WMT'14 En-De task with 6.8x speedup.

Connected through hyperedge HE-1 (induced by source paper "non autoregressive neural dialogue generation"):
  with members:
    - "avgout a simple output probability measure to eliminate dull responses"
    - "towards a human like open domain chatbot"
    - "comparison of diverse decoding methods from conditional language models"
    - "fast structured decoding for sequence models"
    - "complexity weighted loss and diverse reranking for sentence simplification"
    - "pnat non autoregressive transformer by position learning"
    - "dialogpt large scale generative pre training for conversational response generation"
    - "flowseq non autoregressive conditional sequence generation with generative flow"

Connected through hyperedge HE-2 (induced by source paper "parallel machine translation with disentangled context transformer"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "understanding knowledge distillation in non autoregressive machine translation"
    - "mask predict parallel decoding of conditional masked language models"
    - "cloze driven pretraining of self attention networks"
    - "fast structured decoding for sequence models"
    - "levenshtein transformer"
    - "flowseq non autoregressive conditional sequence generation with generative flow"

Connected through hyperedge HE-3 (induced by source paper "on the discrepancy between density estimation and sequence generation"):
  with members:
    - "towards a human like open domain chatbot"
    - "integer discrete flows and lossless compression"
    - "understanding knowledge distillation in non autoregressive machine translation"
    - "molecular geometry prediction using a deep generative graph neural network"
    - "mixture models for diverse machine translation tricks of the trade"
    - "discrete flows invertible generative models of discrete data"
    - "flowseq non autoregressive conditional sequence generation with generative flow"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "semi autoregressive training improves mask predict decoding"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "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-5 (induced by source paper "understanding knowledge distillation in non autoregressive machine translation"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "syntactically supervised transformers for faster neural machine translation"
    - "retrieving sequential information for non autoregressive neural machine translation"
    - "mask predict parallel decoding of conditional masked language models"
    - "imitation learning for non autoregressive neural machine translation"
    - "mixture models for diverse machine translation tricks of the trade"

Connected through hyperedge HE-6 (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 based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "a generalized framework of sequence generation with application to undirected sequence models"):
  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"
    - "pre trained language model representations for language generation"
    - "mask predict parallel decoding of conditional masked language models"
    - "mass masked sequence to sequence pre training for language generation"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "flowseq non autoregressive conditional sequence generation with generative flow"

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)