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: "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis"
  Abstract: Multimodal machine learning is a core research area spanning the language, visual and acoustic modalities. The central challenge in multimodal learning involves learning representations that can process and relate information from multiple modalities. In this paper, we propose two methods for unsupervised learning of joint multimodal representations using sequence to sequence (Seq2Seq) methods: a \textit{Seq2Seq Modality Translation Model} and a \textit{Hierarchical Seq2Seq Modality Translation Model}. We also explore multiple different variations on the multimodal inputs and outputs of these seq2seq models. Our experiments on multimodal sentiment analysis using the CMU-MOSI dataset indicate that our methods learn informative multimodal representations that outperform the baselines and achieve improved performance on multimodal sentiment analysis, specifically in the Bimodal case where our model is able to improve F1 Score by twelve points. We also discuss future directions for multimodal Seq2Seq methods.

Connected through hyperedge HE-1 (induced by source paper "found in translation learning robust joint representations by cyclic translations between modalities"):
  with members:
    - "joint robust voicing detection and pitch estimation based on residual harmonics"
    - "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"
    - "phrase based neural unsupervised machine translation"
    - "learning factorized multimodal representations"
    - "memory fusion network for multi view sequential learning"

Connected through hyperedge HE-2 (induced by source paper "learning factorized multimodal representations"):
  with members:
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "multi attention recurrent network for human communication comprehension"
    - "dependency bottleneck in auto encoding architectures an empirical study"
    - "multimodal local global ranking fusion for emotion recognition"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "memory fusion network for multi view sequential learning"
    - "tensor fusion network for multimodal sentiment analysis"
    - "multimodal machine learning a survey and taxonomy"

Connected through hyperedge HE-3 (induced by source paper "multimodal language analysis with recurrent multistage fusion"):
  with members:
    - "joint robust voicing detection and pitch estimation based on residual harmonics"
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "multimodal local global ranking fusion for emotion recognition"
    - "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-4 (induced by source paper "words can shift dynamically adjusting word representations using nonverbal behaviors"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "multi attention recurrent network for human communication comprehension"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "multimodal local global ranking fusion for emotion recognition"
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "probabilistic fasttext for multi sense word embeddings"
    - "learning factorized multimodal representations"

Connected through hyperedge HE-5 (induced by source paper "an empirical evaluation of sketched svd and its application to leverage score ordering"):
  with members:
    - "learning factorized multimodal representations"
    - "near optimal sketching of low rank tensor regression"
    - "sketching for kronecker product regression and p splines"
    - "practical and optimal lsh for angular distance"
    - "distributed kernel principal component analysis"
    - "speech recognition with deep recurrent neural networks"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-6 (induced by source paper "multimodal local global ranking fusion for emotion recognition"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "multi attention recurrent network for human communication comprehension"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "learning factorized multimodal representations"
    - "memory fusion network for multi view sequential learning"
    - "speech recognition with deep recurrent neural networks"
    - "a survey on multi view learning"

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)