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: "end to end asr free keyword search from speech"
  Abstract: Conventional keyword search (KWS) systems for speech databases match the input text query to the set of word hypotheses generated by an automatic speech recognition (ASR) system from utterances in the database. Hence, such KWS systems attempt to solve the complex problem of ASR as a precursor. Training an ASR system itself is a time-consuming process requiring transcribed speech data. Our prior work presented an ASR-free end-to-end system that needed minimal supervision and trained significantly faster than an ASR-based KWS system. The ASR-free KWS system consisted of three subsystems. The first subsystem was a recurrent neural network based acoustic encoder that extracted a finite-dimensional embedding of the speech utterance. The second subsystem was a query encoder that produced an embedding of the input text query. The acoustic and query embeddings were input to a feedforward neural network that predicted whether the query occurred in the acoustic utterance or not. This paper extends our prior work in several ways. First, we significantly improve upon our previous ASR-free KWS results by nearly 20% relative through improvements to the acoustic encoder. Next, we show that it is possible to train the acoustic encoder on languages other than the language of interest with only a small drop in KWS performance. Finally, we attempt to predict the location of the detected keywords by training a location-sensitive KWS network.

Connected through hyperedge HE-1 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks"):
  with members:
    - "an optimized recurrent unit for ultra low power keyword spotting"
    - "streaming end to end speech recognition for mobile devices"
    - "donut ctc based query by example keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "automatic grammar augmentation for robust voice command recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "spoken language understanding on the edge"

Connected through hyperedge HE-2 (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-3 (induced by source paper "semantic query by example speech search using visual grounding"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "low resource speech to text translation"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "visually grounded cross lingual keyword spotting in speech"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"

Connected through hyperedge HE-4 (induced by source paper "acoustically grounded word embeddings for improved acoustics to word speech recognition"):
  with members:
    - "advancing acoustic to word ctc model"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "hierarchical multitask learning for ctc based speech recognition"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"

Connected through hyperedge HE-5 (induced by source paper "predicting detection filters for small footprint open vocabulary keyword spotting"):
  with members:
    - "donut ctc based query by example keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "automatic grammar augmentation for robust voice command recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "spoken language understanding on the edge"
    - "deep residual learning for small footprint keyword spotting"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-6 (induced by source paper "query by example on device keyword spotting"):
  with members:
    - "donut ctc based query by example keyword spotting"
    - "attention based end to end models for small footprint keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "meta learning for few shot keyword spotting"
    - "fast asr free and almost zero resource keyword spotting using dtw and cnns for humanitarian monitoring"
    - "streaming small footprint keyword spotting using sequence to sequence models"
    - "trainable frontend for robust and far field keyword spotting"

Connected through hyperedge HE-7 (induced by source paper "zero shot keyword spotting for visual speech recognition in the wild"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "espnet end to end speech processing toolkit"
    - "end to end audiovisual speech recognition"
    - "lcanet end to end lipreading with cascaded attention ctc"
    - "improving speaker independent lipreading with domain adversarial training"
    - "attention is all you need"
    - "deep word embeddings for visual speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"

Connected through hyperedge HE-8 (induced by source paper "truly unsupervised acoustic word embeddings using weak top down constraints in encoder decoder models"):
  with members:
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "the zero resource speech challenge 2017"
    - "an embedded segmental k means model for unsupervised segmentation and clustering of speech"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "adam a method for stochastic optimization"

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)