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: "joint robust voicing detection and pitch estimation based on residual harmonics"
  Abstract: This paper focuses on the problem of pitch tracking in noisy conditions. A method using harmonic information in the residual signal is presented. The proposed criterion is used both for pitch estimation, as well as for determining the voicing segments of speech. In the experiments, the method is compared to six state-of-the-art pitch trackers on the Keele and CSTR databases. The proposed technique is shown to be particularly robust to additive noise, leading to a significant improvement in adverse conditions.

Connected through hyperedge HE-1 (induced by source paper "factorized multimodal transformer for multimodal sequential learning"):
  with members:
    - "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "multimodal transformer for unaligned multimodal language sequences"
    - "publicly available clinical bert embeddings"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "multimodal language analysis with recurrent multistage fusion"
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "efficient low rank multimodal fusion with modality specific factors"

Connected through hyperedge HE-2 (induced by source paper "ur funny a multimodal language dataset for understanding humor"):
  with members:
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "multimodal language analysis with recurrent multistage fusion"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "modality based factorization for multimodal fusion"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "words can shift dynamically adjusting word representations using nonverbal behaviors"
    - "learning factorized multimodal representations"

Connected through hyperedge HE-3 (induced by source paper "glottal source processing from analysis to applications"):
  with members:
    - "on the mutual information between source and filter contributions for voice pathology detection"
    - "a comparative evaluation of pitch modification techniques"
    - "phase based information for voice pathology detection"
    - "the deterministic plus stochastic model of the residual signal and its applications"
    - "glottal closure and opening instant detection from speech signals"
    - "causal anticausal decomposition of speech using complex cepstrum for glottal source estimation"
    - "complex cepstrum based decomposition of speech for glottal source estimation"
    - "detection of glottal closure instants from speech signals a quantitative review"

Connected through hyperedge HE-4 (induced by source paper "multimodal deep learning for mental disorders prediction from audio speech samples"):
  with members:
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "multi attention recurrent network for human communication comprehension"
    - "words can shift dynamically adjusting word representations using nonverbal behaviors"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tensor fusion network for multimodal sentiment analysis"
    - "multimodal machine learning a survey and taxonomy"
    - "efficient vector representation for documents through corruption"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-5 (induced by source paper "a comparative study of pitch extraction algorithms on a large variety of singing sounds"):
  with members:
    - "the deterministic plus stochastic model of the residual signal and its applications"

Connected through hyperedge HE-6 (induced by source paper "found in translation learning robust joint representations by cyclic translations between modalities"):
  with members:
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "multimodal language analysis with recurrent multistage fusion"
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "multimodal local global ranking fusion for emotion recognition"
    - "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis"
    - "phrase based neural unsupervised machine translation"
    - "learning factorized multimodal representations"
    - "memory fusion network for multi view sequential learning"

Connected through hyperedge HE-7 (induced by source paper "multimodal language analysis with recurrent multistage fusion"):
  with members:
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "multimodal local global ranking fusion for emotion recognition"
    - "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis"
    - "reusing neural speech representations for auditory emotion recognition"
    - "learning factorized multimodal representations"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "visual features for context aware speech recognition"
    - "tensor fusion network for multimodal sentiment analysis"

Connected through hyperedge HE-8 (induced by source paper "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis"):
  with members:
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "multi attention recurrent network for human communication comprehension"
    - "memory fusion network for multi view sequential learning"
    - "tensor fusion network for multimodal sentiment analysis"
    - "triangle generative adversarial networks"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "triple generative adversarial nets"
    - "cold fusion training seq2seq models together with language models"

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)