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: "very deep multilingual convolutional neural networks for lvcsr"
  Abstract: Convolutional neural networks (CNNs) are a standard component of many current state-of-the-art Large Vocabulary Continuous Speech Recognition (LVCSR) systems. However, CNNs in LVCSR have not kept pace with recent advances in other domains where deeper neural networks provide superior performance. In this paper we propose a number of architectural advances in CNNs for LVCSR. First, we introduce a very deep convolutional network architecture with up to 14 weight layers. There are multiple convolutional layers before each pooling layer, with small 3x3 kernels, inspired by the VGG Imagenet 2014 architecture. Then, we introduce multilingual CNNs with multiple untied layers. Finally, we introduce multi-scale input features aimed at exploiting more context at negligible computational cost. We evaluate the improvements first on a Babel task for low resource speech recognition, obtaining an absolute 5.77% WER improvement over the baseline PLP DNN by training our CNN on the combined data of six different languages. We then evaluate the very deep CNNs on the Hub5'00 benchmark (using the 262 hours of SWB-1 training data) achieving a word error rate of 11.8% after cross-entropy training, a 1.4% WER improvement (10.6% relative) over the best published CNN result so far.

Connected through hyperedge HE-1 (induced by source paper "hydra preserving ensemble diversity for model distillation"):
  with members:
    - "knowledge distillation by on the fly native ensemble"
    - "large scale distributed neural network training through online distillation"
    - "deep mutual learning"
    - "deep exploration via bootstrapped dqn"
    - "bayesian dark knowledge"
    - "fitnets hints for thin deep nets"
    - "going deeper with convolutions"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-2 (induced by source paper "decaying momentum helps neural network training"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "momentum based variance reduction in non convex sgd"
    - "on the convergence of adam and beyond"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "aggregated momentum stability through passive damping"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "negative momentum for improved game dynamics"

Connected through hyperedge HE-3 (induced by source paper "gradient scheduling with global momentum for non iid data distributed asynchronous training"):
  with members:
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"
    - "federated learning with non iid data"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "hello edge keyword spotting on microcontrollers"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "asynchronous stochastic gradient descent with delay compensation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "personalized speech recognition on mobile devices"

Connected through hyperedge HE-4 (induced by source paper "towards efficient and secure delivery of data for deep learning with privacy preserving"):
  with members:
    - "streaming end to end speech recognition for mobile devices"
    - "deep learning a critical appraisal"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "gazelle a low latency framework for secure neural network inference"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "deepsecure scalable provably secure deep learning"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-5 (induced by source paper "negative sampling in semi supervised learning"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "mixup beyond empirical risk minimization"
    - "improved techniques for training gans"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "regularization with stochastic transformations and perturbations for deep semi supervised learning"

Connected through hyperedge HE-6 (induced by source paper "massively multilingual adversarial speech recognition"):
  with members:
    - "transfer learning of language independent end to end asr with language model fusion"
    - "domain adversarial training for accented speech recognition"
    - "hierarchical multitask learning for ctc based speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "listen attend and spell"
    - "attention based models for speech recognition"

Connected through hyperedge HE-7 (induced by source paper "multi scale octave convolutions for robust speech recognition"):
  with members:
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "big little net an efficient multi scale feature representation for visual and speech recognition"
    - "condensenet an efficient densenet using learned group convolutions"
    - "very deep convolutional neural networks for robust speech recognition"
    - "advances in very deep convolutional neural networks for lvcsr"
    - "pruning filters for efficient convnets"

Connected through hyperedge HE-8 (induced by source paper "towards efficient and secure delivery of data for training and inference with privacy preserving"):
  with members:
    - "streaming end to end speech recognition for mobile devices"
    - "deep learning a critical appraisal"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "gazelle a low latency framework for secure neural network inference"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "deepsecure scalable provably secure deep learning"
    - "deep models under the gan information leakage from collaborative deep learning"

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)