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: "multimodal local global ranking fusion for emotion recognition"
  Abstract: Emotion recognition is a core research area at the intersection of artificial intelligence and human communication analysis. It is a significant technical challenge since humans display their emotions through complex idiosyncratic combinations of the language, visual and acoustic modalities. In contrast to traditional multimodal fusion techniques, we approach emotion recognition from both direct person-independent and relative person-dependent perspectives. The direct person-independent perspective follows the conventional emotion recognition approach which directly infers absolute emotion labels from observed multimodal features. The relative person-dependent perspective approaches emotion recognition in a relative manner by comparing partial video segments to determine if there was an increase or decrease in emotional intensity. Our proposed model integrates these direct and relative prediction perspectives by dividing the emotion recognition task into three easier subtasks. The first subtask involves a multimodal local ranking of relative emotion intensities between two short segments of a video. The second subtask uses local rankings to infer global relative emotion ranks with a Bayesian ranking algorithm. The third subtask incorporates both direct predictions from observed multimodal behaviors and relative emotion ranks from local-global rankings for final emotion prediction. Our approach displays excellent performance on an audio-visual emotion recognition benchmark and improves over other algorithms for multimodal fusion.

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"
    - "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-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"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis"
    - "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"
    - "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-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"
    - "deep contextualized word representations"
    - "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "probabilistic fasttext for multi sense word embeddings"
    - "learning factorized multimodal representations"

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.HC(Human-Computer Interaction)