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: "deep convolutional acoustic word embeddings using word pair side information"
  Abstract: Recent studies have been revisiting whole words as the basic modelling unit in speech recognition and query applications, instead of phonetic units. Such whole-word segmental systems rely on a function that maps a variable-length speech segment to a vector in a fixed-dimensional space; the resulting acoustic word embeddings need to allow for accurate discrimination between different word types, directly in the embedding space. We compare several old and new approaches in a word discrimination task. Our best approach uses side information in the form of known word pairs to train a Siamese convolutional neural network (CNN): a pair of tied networks that take two speech segments as input and produce their embeddings, trained with a hinge loss that separates same-word pairs and different-word pairs by some margin. A word classifier CNN performs similarly, but requires much stronger supervision. Both types of CNNs yield large improvements over the best previously published results on the word discrimination task.

Connected through hyperedge HE-1 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages"):
  with members:
    - "learned in speech recognition contextual acoustic word embeddings"
    - "audio linguistic embeddings for spoken sentences"
    - "bayesian subspace hidden markov model for acoustic unit discovery"
    - "massively multilingual adversarial speech recognition"
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "truly unsupervised acoustic word embeddings using weak top down constraints in encoder decoder models"

Connected through hyperedge HE-2 (induced by source paper "supervised speaker embedding de mixing in two speaker environment"):
  with members:
    - "learned in speech recognition contextual acoustic word embeddings"
    - "speaker independent speech separation with deep attractor network"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "voxceleb2 deep speaker recognition"
    - "deep attractor network for single microphone speaker separation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep clustering discriminative embeddings for segmentation and separation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "an unsupervised autoregressive model for speech representation learning"):
  with members:
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "band limited training and inference for convolutional neural networks"):
  with members:
    - "training behavior of deep neural network in frequency domain"
    - "amc automl for model compression and acceleration on mobile devices"
    - "high accuracy low precision training"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "training deep neural networks with 8 bit floating point numbers"
    - "efficient sparse winograd convolutional neural networks"
    - "fft convolutions are faster than winograd on modern cpus here is why"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-5 (induced by source paper "learned in speech recognition contextual acoustic word embeddings"):
  with members:
    - "advancing acoustic to word ctc model"
    - "deep contextualized word representations"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "learning acoustic word embeddings with temporal context for query by example speech search"
    - "how2 a large scale dataset for multimodal language understanding"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "improved training of end to end attention models for speech recognition"

Connected through hyperedge HE-6 (induced by source paper "confusion2vec towards enriching vector space word representations with representational ambiguities"):
  with members:
    - "morphological word embeddings"
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "neural lattice language models"
    - "neural lattice to sequence models for uncertain inputs"
    - "enriching word vectors with subword information"
    - "multi view recurrent neural acoustic word embeddings"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-7 (induced by source paper "completely unsupervised phoneme recognition by a generative adversarial network harmonized with iteratively refined hidden markov models"):
  with members:
    - "completely unsupervised phoneme recognition by adversarially learning mapping relationships from audio embeddings"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "almost unsupervised speech recognition with close to zero resource based on phonetic structures learned from very small unpaired speech and text data"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "unsupervised machine translation using monolingual corpora only"
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"
    - "unsupervised sequence classification using sequential output statistics"

Connected through hyperedge HE-8 (induced by source paper "learning joint acoustic phonetic word embeddings"):
  with members:
    - "a survey of multi view representation learning"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "sampling matters in deep embedding learning"
    - "learning word like units from joint audio visual analysis"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "query by example search with discriminative neural acoustic word embeddings"
    - "learning word embeddings from speech"

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)