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: "dynamic layer normalization for adaptive neural acoustic modeling in speech recognition"
  Abstract: Layer normalization is a recently introduced technique for normalizing the activities of neurons in deep neural networks to improve the training speed and stability. In this paper, we introduce a new layer normalization technique called Dynamic Layer Normalization (DLN) for adaptive neural acoustic modeling in speech recognition. By dynamically generating the scaling and shifting parameters in layer normalization, DLN adapts neural acoustic models to the acoustic variability arising from various factors such as speakers, channel noises, and environments. Unlike other adaptive acoustic models, our proposed approach does not require additional adaptation data or speaker information such as i-vectors. Moreover, the model size is fixed as it dynamically generates adaptation parameters. We apply our proposed DLN to deep bidirectional LSTM acoustic models and evaluate them on two benchmark datasets for large vocabulary ASR experiments: WSJ and TED-LIUM release 2. The experimental results show that our DLN improves neural acoustic models in terms of transcription accuracy by dynamically adapting to various speakers and environments.

Connected through hyperedge HE-1 (induced by source paper "positional normalization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "pay less attention with lightweight and dynamic convolutions"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "differentiable learning to normalize via switchable normalization"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "characterizing deep learning training workloads on alibaba pai"):
  with members:
    - "scalable deep learning on distributed infrastructures challenges techniques and tools"
    - "data motif based proxy benchmarks for big data and ai workloads"
    - "graph convolutional neural networks for web scale recommender systems"
    - "communication scheduling as a first class citizen in distributed machine learning systems"
    - "data motifs a lens towards fully understanding big data and ai workloads"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fusionstitching deep fusion and code generation for tensorflow computations on gpus"
    - "predicting the computational cost of deep learning models"

Connected through hyperedge HE-3 (induced by source paper "ensemble model patching a parameter efficient variational bayesian neural network"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "an empirical model of large batch training"
    - "adversarial distillation of bayesian neural network posteriors"
    - "a style based generator architecture for generative adversarial networks"
    - "averaging weights leads to wider optima and better generalization"
    - "k for the price of 1 parameter efficient multi task and transfer learning"

Connected through hyperedge HE-4 (induced by source paper "temporal film capturing long range sequence dependencies with feature wise modulations"):
  with members:
    - "how to fine tune bert for text classification"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "film visual reasoning with a general conditioning layer"
    - "convolutional sequence to sequence learning"
    - "very deep convolutional networks for natural language processing"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "instance level meta normalization"):
  with members:
    - "group normalization"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "decorrelated batch normalization"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "mask r cnn"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-6 (induced by source paper "embeddings for dnn speaker adaptive training"):
  with members:
    - "analyzing deep cnn based utterance embeddings for acoustic model adaptation"
    - "voxceleb2 deep speaker recognition"
    - "embedding based speaker adaptive training of deep neural networks"
    - "environmental noise embeddings for robust speech recognition"
    - "learning hidden unit contributions for unsupervised acoustic model adaptation"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "on self modulation for generative adversarial networks"):
  with members:
    - "cgans with projection discriminator"
    - "the gan landscape losses architectures regularization and normalization"
    - "spectral normalization for generative adversarial networks"
    - "deep generative models for distribution preserving lossy compression"
    - "understanding the effectiveness of lipschitz continuity in generative adversarial nets"
    - "modulating early visual processing by language"
    - "progressive growing of gans for improved quality stability and variation"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "film visual reasoning with a general conditioning layer"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "modulating early visual processing by language"
    - "inferring and executing programs for visual reasoning"
    - "visual interaction networks"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "learning visual reasoning without strong priors"

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)