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: "feature learning in deep neural networks studies on speech recognition tasks"
  Abstract: Recent studies have shown that deep neural networks (DNNs) perform significantly better than shallow networks and Gaussian mixture models (GMMs) on large vocabulary speech recognition tasks. In this paper, we argue that the improved accuracy achieved by the DNNs is the result of their ability to extract discriminative internal representations that are robust to the many sources of variability in speech signals. We show that these representations become increasingly insensitive to small perturbations in the input with increasing network depth, which leads to better speech recognition performance with deeper networks. We also show that DNNs cannot extrapolate to test samples that are substantially different from the training examples. If the training data are sufficiently representative, however, internal features learned by the DNN are relatively stable with respect to speaker differences, bandwidth differences, and environment distortion. This enables DNN-based recognizers to perform as well or better than state-of-the-art systems based on GMMs or shallow networks without the need for explicit model adaptation or feature normalization.

Connected through hyperedge HE-1 (induced by source paper "latent weights do not exist rethinking binarized neural network optimization"):
  with members:
    - "back to simplicity how to train accurate bnns from scratch"
    - "improved training of binary networks for human pose estimation and image recognition"
    - "regularizing activation distribution for training binarized deep networks"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "model compression via distillation and quantization"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "probabilistic binary neural networks"
    - "structured binary neural networks for accurate image classification and semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "high performance deep learning via a single building block"):
  with members:
    - "stripe tensor compilation via the nested polyhedral model"
    - "large batch training for lstm and beyond"
    - "nvidia tensor core programmability performance precision"
    - "high accuracy low precision training"
    - "glow graph lowering compiler techniques for neural networks"
    - "anatomy of high performance deep learning convolutions on simd architectures"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "deep model reference adaptive control"):
  with members:
    - "generalization error in deep learning"
    - "understanding deep learning requires rethinking generalization"
    - "benefits of depth in neural networks"
    - "learning functions when is deep better than shallow"
    - "robustness and generalization"

Connected through hyperedge HE-4 (induced by source paper "learning discriminative features in sequence training without requiring framewise labelled data"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-5 (induced by source paper "toward domain invariant speech recognition via large scale training"):
  with members:
    - "generalizing across domains via cross gradient training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "english conversational telephone speech recognition by humans and machines"
    - "adversarial discriminative domain adaptation"
    - "large scale domain adaptation via teacher student learning"
    - "comparing human and machine errors in conversational speech transcription"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"

Connected through hyperedge HE-6 (induced by source paper "unsupervised learning of disentangled and interpretable representations from sequential data"):
  with members:
    - "toward controlled generation of text"
    - "unsupervised domain adaptation for robust speech recognition via variational autoencoder based data augmentation"
    - "learning latent representations for speech generation and transformation"
    - "pixel recurrent neural networks"
    - "hierarchical multiscale recurrent neural networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep convolutional inverse graphics network"
    - "a recurrent latent variable model for sequential data"

Connected through hyperedge HE-7 (induced by source paper "unsupervised neural and bayesian models for zero resource speech processing"):
  with members:
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "unsupervised word segmentation and lexicon discovery using acoustic word embeddings"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "symbol emergence in robotics a survey"
    - "unsupervised discovery of linguistic structure including two level acoustic patterns using three cascaded stages of iterative optimization"
    - "deep multimodal semantic embeddings for speech and images"
    - "deep convolutional acoustic word embeddings using word pair side information"
    - "a statistical model for word discovery in transcribed speech"

Connected through hyperedge HE-8 (induced by source paper "efficient implementation of the room simulator for training deep neural network acoustic models"):
  with members:
    - "fast and accurate recurrent neural network acoustic models for speech recognition"

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)