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: "transfer learning for speech recognition on a budget"
  Abstract: End-to-end training of automated speech recognition (ASR) systems requires massive data and compute resources. We explore transfer learning based on model adaptation as an approach for training ASR models under constrained GPU memory, throughput and training data. We conduct several systematic experiments adapting a Wav2Letter convolutional neural network originally trained for English ASR to the German language. We show that this technique allows faster training on consumer-grade resources while requiring less training data in order to achieve the same accuracy, thereby lowering the cost of training ASR models in other languages. Model introspection revealed that small adaptations to the network's weights were sufficient for good performance, especially for inner layers.

Connected through hyperedge HE-1 (induced by source paper "gradient adjusted neuron activation profiles for comprehensive introspection of convolutional speech recognition models"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "sanity checks for saliency maps"
    - "wav2letter an end to end convnet based speech recognition system"
    - "understanding neural networks through deep visualization"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "regula sub rosa latent backdoor attacks on deep neural networks"):
  with members:
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "adversarial patch"
    - "adversarial logit pairing"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "hu fu hardware and software collaborative attack framework against neural networks"
    - "spectral signatures in backdoor attacks"
    - "hardware trojan attacks on neural networks"

Connected through hyperedge HE-3 (induced by source paper "wav2vec unsupervised pre training for speech recognition"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pre trained language model representations for language generation"
    - "cloze driven pretraining of self attention networks"
    - "who needs words lexicon free speech recognition"
    - "data efficient image recognition with contrastive predictive coding"
    - "cross lingual language model pretraining"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "improving speech emotion recognition via transformer based predictive coding through transfer learning"

Connected through hyperedge HE-4 (induced by source paper "prototypical metric transfer learning for continuous speech keyword spotting with limited training data"):
  with members:
    - "training neural audio classifiers with few data"
    - "prototypical networks for few shot learning"
    - "honk a pytorch reimplementation of convolutional neural networks for keyword spotting"
    - "transfer learning for music classification and regression tasks"
    - "matching networks for one shot learning"
    - "deep residual learning for image recognition"
    - "deep speech scaling up end to end speech recognition"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-5 (induced by source paper "cantonese automatic speech recognition using transfer learning from mandarin"):
  with members:
    - "trivial transfer learning for low resource neural machine translation"
    - "aishell 2 transforming mandarin asr research into industrial scale"
    - "transfer learning for speech and language processing"

Connected through hyperedge HE-6 (induced by source paper "training neural audio classifiers with few data"):
  with members:
    - "randomly weighted cnns for music audio classification"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "deep learning for audio transcription on low resource datasets"
    - "data efficient weakly supervised learning for low resource audio event detection using deep learning"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "unsupervised learning of semantic audio representations"
    - "language and noise transfer in speech enhancement generative adversarial network"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-7 (induced by source paper "freezing subnetworks to analyze domain adaptation in neural machine translation"):
  with members:
    - "extreme adaptation for personalized neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "regularization techniques for fine tuning in neural machine translation"
    - "transfer learning across low resource related languages for neural machine translation"
    - "fast domain adaptation for neural machine translation"
    - "one sentence one model for neural machine translation"
    - "transfer learning for low resource neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "exploiting nontrivial connectivity for automatic speech recognition"):
  with members:
    - "memory efficient implementation of densenets"
    - "wav2letter an end to end convnet based speech recognition system"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "the power of depth for feedforward neural networks"
    - "deep residual learning for image recognition"
    - "training very deep networks"
    - "first pass large vocabulary continuous speech recognition using bi directional recurrent dnns"

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)