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: "parsing speech a neural approach to integrating lexical and acoustic prosodic information"
  Abstract: In conversational speech, the acoustic signal provides cues that help listeners disambiguate difficult parses. For automatically parsing spoken utterances, we introduce a model that integrates transcribed text and acoustic-prosodic features using a convolutional neural network over energy and pitch trajectories coupled with an attention-based recurrent neural network that accepts text and prosodic features. We find that different types of acoustic-prosodic features are individually helpful, and together give statistically significant improvements in parse and disfluency detection F1 scores over a strong text-only baseline. For this study with known sentence boundaries, error analyses show that the main benefit of acoustic-prosodic features is in sentences with disfluencies, attachment decisions are most improved, and transcription errors obscure gains from prosody.

Connected through hyperedge HE-1 (induced by source paper "identification of primary and collateral tracks in stuttered speech"):
  with members:
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "disfluency detection using a bidirectional lstm"
    - "imbalanced learn a python toolbox to tackle the curse of imbalanced datasets in machine learning"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "improving joint training of inference networks and structured prediction energy networks"):
  with members:
    - "benchmarking approximate inference methods for neural structured prediction"
    - "spectral normalization for generative adversarial networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning approximate inference networks for structured prediction"
    - "learning to embed words in context for syntactic tasks"
    - "improved training of wasserstein gans"
    - "energy based generative adversarial network"
    - "improved techniques for training gans"

Connected through hyperedge HE-3 (induced by source paper "from semi supervised to almost unsupervised speech recognition with very low resource by jointly learning phonetic structures from audio and text embeddings"):
  with members:
    - "completely unsupervised phoneme recognition by adversarially learning mapping relationships from audio embeddings"
    - "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"
    - "almost unsupervised speech recognition with close to zero resource based on phonetic structures learned from very small unpaired speech and text data"
    - "end to end neural segmental models for speech recognition"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "improved training of wasserstein gans"
    - "an embedded segmental k means model for unsupervised segmentation and clustering of speech"

Connected through hyperedge HE-4 (induced by source paper "modeling acoustic prosodic cues for word importance prediction in spoken dialogues"):
  with members:
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "a corpus for modeling word importance in spoken dialogue transcripts"
    - "evaluating the usability of automatically generated captions for people who are deaf or hard of hearing"
    - "joint modeling of text and acoustic prosodic cues for neural parsing"
    - "multi view recurrent neural acoustic word embeddings"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-5 (induced by source paper "giving attention to the unexpected using prosody innovations in disfluency detection"):
  with members:
    - "multi attention recurrent network for human communication comprehension"
    - "robust cross domain disfluency detection with pattern match networks"
    - "disfluency detection using auto correlational neural networks"
    - "disfluency detection using a noisy channel model and a deep neural language model"
    - "tensor fusion network for multimodal sentiment analysis"
    - "disfluency detection using a bidirectional lstm"
    - "show attend and tell neural image caption generation with visual attention"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-6 (induced by source paper "neural constituency parsing of speech transcripts"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "constituency parsing with a self attentive encoder"
    - "disfluency detection using a noisy channel model and a deep neural language model"
    - "attention is all you need"
    - "a minimal span based neural constituency parser"
    - "disfluency detection using a bidirectional lstm"

Connected through hyperedge HE-7 (induced by source paper "disfluencies and human speech transcription errors"):
  with members:
    - "giving attention to the unexpected using prosody innovations in disfluency detection"
    - "robust cross domain disfluency detection with pattern match networks"
    - "english conversational telephone speech recognition by humans and machines"
    - "comparing human and machine errors in conversational speech transcription"
    - "disfluency detection using a bidirectional lstm"
    - "achieving human parity in conversational speech recognition"

Connected through hyperedge HE-8 (induced by source paper "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"):
  with members:
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "an iterative closest point method for unsupervised word translation"
    - "non adversarial unsupervised word translation"
    - "towards unsupervised automatic speech recognition trained by unaligned speech and text only"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "unsupervised learning of semantic audio representations"
    - "end to end neural segmental 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.CL(Computation and Language)