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: "transferring knowledge from a rnn to a dnn"
  Abstract: Deep Neural Network (DNN) acoustic models have yielded many state-of-the-art results in Automatic Speech Recognition (ASR) tasks. More recently, Recurrent Neural Network (RNN) models have been shown to outperform DNNs counterparts. However, state-of-the-art DNN and RNN models tend to be impractical to deploy on embedded systems with limited computational capacity. Traditionally, the approach for embedded platforms is to either train a small DNN directly, or to train a small DNN that learns the output distribution of a large DNN. In this paper, we utilize a state-of-the-art RNN to transfer knowledge to small DNN. We use the RNN model to generate soft alignments and minimize the Kullback-Leibler divergence against the small DNN. The small DNN trained on the soft RNN alignments achieved a 3.93 WER on the Wall Street Journal (WSJ) eval92 task compared to a baseline 4.54 WER or more than 13% relative improvement.

Connected through hyperedge HE-1 (induced by source paper "deepmimic mentor student unlabeled data based training"):
  with members:
    - "stealing knowledge from protected deep neural networks using composite unlabeled data"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "robust student network learning"
    - "learning transferable architectures for scalable image recognition"
    - "low shot learning with imprinted weights"
    - "mixup beyond empirical risk minimization"
    - "squeeze and excitation networks"

Connected through hyperedge HE-2 (induced by source paper "the pupil has become the master teacher student model based word embedding distillation with ensemble learning"):
  with members:
    - "dynamic routing between capsules"
    - "enriching word vectors with subword information"
    - "deep model compression distilling knowledge from noisy teachers"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "distilling word embeddings an encoding approach"
    - "fitnets hints for thin deep nets"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "conditional teacher student learning"):
  with members:
    - "attentive adversarial learning for domain invariant training"
    - "adversarial speaker adaptation"
    - "developing far field speaker system via teacher student learning"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "a teacher student framework for zero resource neural machine translation"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "large scale domain adaptation via teacher student learning"
    - "sequence level knowledge distillation"

Connected through hyperedge HE-4 (induced by source paper "recurrent knowledge distillation"):
  with members:
    - "sharesnet reducing residual network parameter number by sharing weights"
    - "mask r cnn"
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "resfeats residual network based features for image classification"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "sketching and neural networks"
    - "highway and residual networks learn unrolled iterative estimation"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-5 (induced by source paper "distilling knowledge using parallel data for far field speech recognition"):
  with members:
    - "a network of deep neural networks for distant speech recognition"
    - "the ibm 2016 english conversational telephone speech recognition system"
    - "the microsoft 2016 conversational speech recognition system"
    - "recurrent models for auditory attention in multi microphone distance speech recognition"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-6 (induced by source paper "developing far field speaker system via teacher student learning"):
  with members:
    - "advancing acoustic to word ctc model"
    - "advancing connectionist temporal classification with attention modeling"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "large scale domain adaptation via teacher student learning"
    - "maximum a posteriori adaptation of network parameters in deep models"

Connected through hyperedge HE-7 (induced by source paper "improved training for online end to end speech recognition systems"):
  with members:
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "reducing bias in production speech models"
    - "regularizing neural networks by penalizing confident output distributions"
    - "sequence level knowledge distillation"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "listen attend and spell"

Connected through hyperedge HE-8 (induced by source paper "small footprint highway deep neural networks for speech recognition"):
  with members:
    - "knowledge distillation for small footprint highway networks"
    - "sequence training and adaptation of highway deep neural networks"
    - "recurrent highway networks"
    - "the ibm 2016 english conversational telephone speech recognition system"
    - "learning hidden unit contributions for unsupervised acoustic model adaptation"
    - "acdc a structured efficient linear layer"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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.LG(Machine Learning)