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: "a bit of progress in language modeling"
  Abstract: In the past several years, a number of different language modeling improvements over simple trigram models have been found, including caching, higher-order n-grams, skipping, interpolated Kneser-Ney smoothing, and clustering. We present explorations of variations on, or of the limits of, each of these techniques, including showing that sentence mixture models may have more potential. While all of these techniques have been studied separately, they have rarely been studied in combination. We find some significant interactions, especially with smoothing and clustering techniques. We compare a combination of all techniques together to a Katz smoothed trigram model with no count cutoffs. We achieve perplexity reductions between 38% and 50% (1 bit of entropy), depending on training data size, as well as a word error rate reduction of 8.9%. Our perplexity reductions are perhaps the highest reported compared to a fair baseline. This is the extended version of the paper; it contains additional details and proofs, and is designed to be a good introduction to the state of the art in language modeling.

Connected through hyperedge HE-1 (induced by source paper "scaling laws for neural language models"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "growing a brain fine tuning by increasing model capacity"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "autogrow automatic layer growing in deep convolutional networks"
    - "a constructive prediction of the generalization error across scales"
    - "universal transformers"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-2 (induced by source paper "montage a neural network language model guided javascript engine fuzzer"):
  with members:
    - "evaluating fuzz testing"
    - "learn fuzz machine learning for input fuzzing"
    - "exploring the limits of language modeling"
    - "character aware neural language models"
    - "structured generative models of natural source code"

Connected through hyperedge HE-3 (induced by source paper "effective estimation of deep generative language models"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "preventing posterior collapse with delta vaes"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "neural autoregressive flows"
    - "a hierarchical latent structure for variational conversation modeling"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "spherical latent spaces for stable variational autoencoders"

Connected through hyperedge HE-4 (induced by source paper "augmenting self attention with persistent memory"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "adaptive attention span in transformers"
    - "fast parametric learning with activation memorization"
    - "self attention with relative position representations"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "goal embedded dual hierarchical model for task oriented dialogue generation"):
  with members:
    - "a repository of conversational datasets"
    - "simple fast accurate intent classification and slot labeling for goal oriented dialogue systems"
    - "simple fast accurate intent classification and slot labeling"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "a hierarchical latent structure for variational conversation modeling"
    - "global locally self attentive dialogue state tracker"
    - "on the state of the art of evaluation in neural language models"

Connected through hyperedge HE-6 (induced by source paper "error correcting neural sequence prediction"):
  with members:
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "neural machine translation via binary code prediction"
    - "differentiable scheduled sampling for credit assignment"
    - "compressing word embeddings via deep compositional code learning"
    - "efficient softmax approximation for gpus"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "strategies for training large vocabulary neural language models"
    - "scheduled sampling for sequence prediction with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "machine translation a literature review"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "unsupervised neural machine translation"
    - "one model to learn them all"
    - "effective approaches to attention based neural machine translation"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "on using very large target vocabulary for neural machine translation"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-8 (induced by source paper "learning deterministic weighted automata with queries and counterexamples"):
  with members:
    - "weighted automata extraction from recurrent neural networks via regression on state spaces"
    - "explaining black boxes on sequential data using weighted automata"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "connecting weighted automata and recurrent neural networks through spectral learning"
    - "attention is all you need"
    - "a maximum matching algorithm for basis selection in spectral learning"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "prediction with a short memory"

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)