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 affective analysis using hierarchical attention strategy with word level alignment"
  Abstract: Multimodal affective computing, learning to recognize and interpret human affects and subjective information from multiple data sources, is still challenging because: (i) it is hard to extract informative features to represent human affects from heterogeneous inputs; (ii) current fusion strategies only fuse different modalities at abstract level, ignoring time-dependent interactions between modalities. Addressing such issues, we introduce a hierarchical multimodal architecture with attention and word-level fusion to classify utter-ance-level sentiment and emotion from text and audio data. Our introduced model outperforms the state-of-the-art approaches on published datasets and we demonstrated that our model is able to visualize and interpret the synchronized attention over modalities.

Connected through hyperedge HE-1 (induced by source paper "cross modal learning for multi modal video categorization"):
  with members:
    - "learning relationships between text audio and video via deep canonical correlation for multimodal language analysis"
    - "bert for large scale video segment classification with test time augmentation"
    - "multimodal fusion with deep neural networks for audio video emotion recognition"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "fully automatic video colorization with self regularization and diversity"
    - "making sense of vision and touch learning multimodal representations for contact rich tasks"
    - "videobert a joint model for video and language representation learning"
    - "cooperative learning of audio and video models from self supervised synchronization"

Connected through hyperedge HE-2 (induced by source paper "learning representations from imperfect time series data via tensor rank regularization"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "low rank tensor completion for multiway visual data"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "tensor regression networks"
    - "tensor fusion network for multimodal sentiment analysis"
    - "weighted low rank tensor recovery for hyperspectral image restoration"
    - "multimodal compact bilinear pooling for multimodal neural machine translation"
    - "tensor product generation networks"

Connected through hyperedge HE-3 (induced by source paper "incorporating end to end speech recognition models for sentiment analysis"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "on the robustness of speech emotion recognition for human robot interaction with deep neural networks"
    - "multi attention recurrent network for human communication comprehension"
    - "ted lium 3 twice as much data and corpus repartition for experiments on speaker adaptation"
    - "getting the subtext without the text scalable multimodal sentiment classification from visual and acoustic modalities"
    - "reusing neural speech representations for auditory emotion recognition"
    - "cnn lstm architecture for speech emotion recognition with data augmentation"

Connected through hyperedge HE-4 (induced by source paper "modality to modality translation an adversarial representation learning and graph fusion network for multimodal fusion"):
  with members:
    - "strong and simple baselines for multimodal utterance embeddings"
    - "multimodal transformer for unaligned multimodal language sequences"
    - "multimodal sentiment analysis using hierarchical fusion with context modeling"
    - "multimodal language analysis with recurrent multistage fusion"
    - "multi attention recurrent network for human communication comprehension"
    - "investigating audio visual and text fusion methods for end to end automatic personality prediction"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "efficient low rank multimodal fusion with modality specific factors"

Connected through hyperedge HE-5 (induced by source paper "multimodal transformer for unaligned multimodal language sequences"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "words can shift dynamically adjusting word representations using nonverbal behaviors"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "linguistically informed self attention for semantic role labeling"
    - "adaptive input representations for neural language modeling"

Connected through hyperedge HE-6 (induced by source paper "speech sentiment analysis via pre trained features from end to end asr models"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "incorporating end to end speech recognition models for sentiment analysis"
    - "attention augmented end to end multi task learning for emotion prediction from speech"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "attention is all you need"
    - "a structured self attentive sentence embedding"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "disambiguating speech intention via audio text co attention framework a case of prosody semantics interface"):
  with members:
    - "investigating an effective character level embedding in korean sentence classification"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "predicting prosodic prominence from text with pre trained contextualized word representations"
    - "speech intention understanding in a head final language a disambiguation utilizing intonation dependency"
    - "real time automatic word segmentation for user generated text"
    - "a structured self attentive sentence embedding"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-8 (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 local global ranking fusion for emotion recognition"
    - "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.CL(Computation and Language)