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 sentiment analysis with word level fusion and reinforcement learning"
  Abstract: With the increasing popularity of video sharing websites such as YouTube and Facebook, multimodal sentiment analysis has received increasing attention from the scientific community. Contrary to previous works in multimodal sentiment analysis which focus on holistic information in speech segments such as bag of words representations and average facial expression intensity, we develop a novel deep architecture for multimodal sentiment analysis that performs modality fusion at the word level. In this paper, we propose the Gated Multimodal Embedding LSTM with Temporal Attention (GME-LSTM(A)) model that is composed of 2 modules. The Gated Multimodal Embedding alleviates the difficulties of fusion when there are noisy modalities. The LSTM with Temporal Attention performs word level fusion at a finer fusion resolution between input modalities and attends to the most important time steps. As a result, the GME-LSTM(A) is able to better model the multimodal structure of speech through time and perform better sentiment comprehension. We demonstrate the effectiveness of this approach on the publicly-available Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis (CMU-MOSI) dataset by achieving state-of-the-art sentiment classification and regression results. Qualitative analysis on our model emphasizes the importance of the Temporal Attention Layer in sentiment prediction because the additional acoustic and visual modalities are noisy. We also demonstrate the effectiveness of the Gated Multimodal Embedding in selectively filtering these noisy modalities out. Our results and analysis open new areas in the study of sentiment analysis in human communication and provide new models for multimodal fusion.

Connected through hyperedge HE-1 (induced by source paper "gated mechanism for attention based multimodal sentiment analysis"):
  with members:
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "multi attention recurrent network for human communication comprehension"
    - "memory fusion network for multi view sequential learning"
    - "tensor fusion network for multimodal sentiment analysis"
    - "ruminating reader reasoning with gated multi hop attention"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (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"
    - "joint robust voicing detection and pitch estimation based on residual harmonics"
    - "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"
    - "efficient low rank multimodal fusion with modality specific factors"

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"
    - "on the robustness of speech emotion recognition for human robot interaction with deep neural networks"
    - "multi attention recurrent network for human communication comprehension"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "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 "learning relationships between text audio and video via deep canonical correlation for multimodal language analysis"):
  with members:
    - "multi modal sentiment analysis using deep canonical correlation analysis"
    - "multimodal transformer for unaligned multimodal language sequences"
    - "multimodal language analysis with recurrent multistage fusion"
    - "multi attention recurrent network for human communication comprehension"
    - "domain adapted word embeddings for improved sentiment classification"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "words can shift dynamically adjusting word representations using nonverbal behaviors"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "pseudo encoded stochastic variational inference"):
  with members:
    - "variational auto decoder"
    - "inference suboptimality in variational autoencoders"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (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 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 "learning factorized multimodal representations"):
  with members:
    - "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"
    - "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-8 (induced by source paper "centralnet a multilayer approach for multimodal fusion"):
  with members:
    - "efficient large scale multi modal classification"
    - "multimodal machine learning a survey and taxonomy"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "truly multi modal youtube 8m video classification with video audio and text"
    - "youtube 8m a large scale video classification benchmark"
    - "correlational neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "moddrop adaptive multi modal gesture recognition"

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.LG(Machine Learning)