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: "unsupervised speech representation learning using wavenet autoencoders"
  Abstract: We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms. The goal is to learn a representation able to capture high level semantic content from the signal, e.g. phoneme identities, while being invariant to confounding low level details in the signal such as the underlying pitch contour or background noise. Since the learned representation is tuned to contain only phonetic content, we resort to using a high capacity WaveNet decoder to infer information discarded by the encoder from previous samples. Moreover, the behavior of autoencoder models depends on the kind of constraint that is applied to the latent representation. We compare three variants: a simple dimensionality reduction bottleneck, a Gaussian Variational Autoencoder (VAE), and a discrete Vector Quantized VAE (VQ-VAE). We analyze the quality of learned representations in terms of speaker independence, the ability to predict phonetic content, and the ability to accurately reconstruct individual spectrogram frames. Moreover, for discrete encodings extracted using the VQ-VAE, we measure the ease of mapping them to phonemes. We introduce a regularization scheme that forces the representations to focus on the phonetic content of the utterance and report performance comparable with the top entries in the ZeroSpeech 2017 unsupervised acoustic unit discovery task.

Connected through hyperedge HE-1 (induced by source paper "from abstract items to latent spaces to observed data and back compositional variational auto encoder"):
  with members:
    - "deep learning for generic object detection a survey"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "emergence of invariance and disentanglement in deep representations"
    - "neural message passing for quantum chemistry"
    - "opening the black box of deep neural networks via information"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-2 (induced by source paper "on completeness aware concept based explanations in deep neural networks"):
  with members:
    - "educe explaining model decisions through unsupervised concepts extraction"
    - "attention based prototypical learning towards interpretable confident and robust deep neural networks"
    - "evaluating explanation without ground truth in interpretable machine learning"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "representer point selection for explaining deep neural networks"
    - "explaining explanations an overview of interpretability of machine learning"
    - "learning to generate reviews and discovering sentiment"
    - "l shapley and c shapley efficient model interpretation for structured data"

Connected through hyperedge HE-3 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech"):
  with members:
    - "unsupervised acoustic unit discovery for speech synthesis using discrete latent variable neural networks"
    - "models of visually grounded speech signal pay attention to nouns a bilingual experiment on english and japanese"
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "unsupervised end to end learning of discrete linguistic units for voice conversion"
    - "language learning using speech to image retrieval"
    - "towards visually grounded sub word speech unit discovery"
    - "the zero resource speech challenge 2019 tts without t"
    - "word recognition competition and activation in a model of visually grounded speech"

Connected through hyperedge HE-4 (induced by source paper "deep learning for deepfakes creation and detection"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "on the generalization of gan image forensics"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "few shot adversarial learning of realistic neural talking head models"
    - "limits of deepfake detection a robust estimation viewpoint"
    - "exposing deep fakes using inconsistent head poses"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "exposing deepfake videos by detecting face warping artifacts"

Connected through hyperedge HE-5 (induced by source paper "an unsupervised autoregressive model for speech representation learning"):
  with members:
    - "a theoretical analysis of contrastive unsupervised representation learning"
    - "unspeech unsupervised speech context embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "dissecting contextual word embeddings architecture and representation"
    - "universal language model fine tuning for text classification"
    - "on training recurrent networks with truncated backpropagation through time in speech recognition"

Connected through hyperedge HE-6 (induced by source paper "learning problem agnostic speech representations from multiple self supervised tasks"):
  with members:
    - "a survey of recent dnn architectures on the timit phone recognition task"
    - "unsupervised representation learning by predicting image rotations"
    - "representation learning with contrastive predictive coding"
    - "towards a universal neural network encoder for time series"
    - "objects that sound"
    - "learning sight from sound ambient sound provides supervision for visual learning"
    - "unsupervised learning of semantic audio representations"
    - "segan speech enhancement generative adversarial network"

Connected through hyperedge HE-7 (induced by source paper "unsupervised end to end learning of discrete linguistic units for voice conversion"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "decoupled learning for conditional adversarial networks"
    - "improved audio embeddings by adjacency based clustering with applications in spoken term detection"
    - "voice impersonation using generative adversarial networks"
    - "unsupervised learning of semantic audio representations"
    - "tacotron towards end to end speech synthesis"
    - "improved training of wasserstein gans"
    - "deep voice 3 2000 speaker neural text to speech"

Connected through hyperedge HE-8 (induced by source paper "the zero resource speech challenge 2019 tts without t"):
  with members:
    - "unsupervised acoustic unit discovery for speech synthesis using discrete latent variable neural networks"
    - "vqvae unsupervised unit discovery and multi scale code2spec inverter for zerospeech challenge 2019"
    - "unsupervised end to end learning of discrete linguistic units for voice conversion"
    - "close to human quality tts with transformer"
    - "bayesian models for unit discovery on a very low resource language"
    - "voice impersonation using generative adversarial networks"
    - "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop"
    - "listening while speaking speech chain by 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.LG(Machine Learning)