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: "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"
  Abstract: In this paper we analyze the gate activation signals inside the gated recurrent neural networks, and find the temporal structure of such signals is highly correlated with the phoneme boundaries. This correlation is further verified by a set of experiments for phoneme segmentation, in which better results compared to standard approaches were obtained.

Connected through hyperedge HE-1 (induced by source paper "untangling in invariant speech recognition"):
  with members:
    - "analyzing learned representations of a deep asr performance prediction model"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "deep speaker an end to end neural speaker embedding system"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "geodesics of learned representations"
    - "convergent learning do different neural networks learn the same representations"

Connected through hyperedge HE-2 (induced by source paper "on the linguistic representational power of neural machine translation models"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"
    - "a hybrid morpheme word representation for machine translation of morphologically rich languages"
    - "linguistic knowledge and transferability of contextual representations"
    - "the missing ingredient in zero shot neural machine translation"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "bpe and charcnns for translation of morphology a cross lingual comparison and analysis"

Connected through hyperedge HE-3 (induced by source paper "self supervised speech recognition via local prior matching"):
  with members:
    - "attentive adversarial learning for domain invariant training"
    - "parallel neural text to speech"
    - "libri light a benchmark for asr with limited or no supervision"
    - "lessons from building acoustic models with a million hours of speech"
    - "a fully differentiable beam search decoder"
    - "back translation style data augmentation for end to end asr"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "cycle consistency training for end to end speech recognition"

Connected through hyperedge HE-4 (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"
    - "unsupervised sequence classification using sequential output statistics"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "analyzing phonetic and graphemic representations in end to end automatic speech recognition"):
  with members:
    - "towards end to end code switching speech recognition"
    - "analyzing learned representations of a deep asr performance prediction model"
    - "stream attention based multi array end to end speech recognition"
    - "phonetic and graphemic systems for multi genre broadcast transcription"
    - "analysis methods in neural language processing a survey"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "learning word like units from joint audio visual analysis"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"

Connected through hyperedge HE-6 (induced by source paper "a critical analysis of biased parsers in unsupervised parsing"):
  with members:
    - "an imitation learning approach to unsupervised parsing"
    - "visually grounded neural syntax acquisition"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "grammar induction with neural language models an unusual replication"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "a minimal span based neural constituency parser"
    - "neural language modeling by jointly learning syntax and lexicon"

Connected through hyperedge HE-7 (induced by source paper "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"):
  with members:
    - "revisiting the importance of individual units in cnns via ablation"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "identifying and controlling important neurons in neural machine translation"
    - "learning to generate reviews and discovering sentiment"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "neurox a toolkit for analyzing individual neurons in neural networks"
    - "attention is all you need"
    - "what do neural machine translation models learn about morphology"

Connected through hyperedge HE-8 (induced by source paper "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"):
  with members:
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "encoding of phonology in a recurrent neural model of grounded speech"
    - "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"
    - "semantic tagging with deep residual networks"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "representation of linguistic form and function in recurrent neural networks"
    - "word representation models for morphologically rich languages in neural machine translation"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"

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.SD(Sound)