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: "tensor fusion network for multimodal sentiment analysis"
  Abstract: Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multimodal setup where other relevant modalities accompany language. In this paper, we pose the problem of multimodal sentiment analysis as modeling intra-modality and inter-modality dynamics. We introduce a novel model, termed Tensor Fusion Network, which learns both such dynamics end-to-end. The proposed approach is tailored for the volatile nature of spoken language in online videos as well as accompanying gestures and voice. In the experiments, our model outperforms state-of-the-art approaches for both multimodal and unimodal sentiment analysis.

Connected through hyperedge HE-1 (induced by source paper "fgn fusion glyph network for chinese named entity recognition"):
  with members:
    - "glyce glyph vectors for chinese character representations"
    - "chinese ner using lattice lstm"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "memory fusion network for multi view sequential learning"
    - "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"
    - "attention is all you need"
    - "glyph aware embedding of chinese characters"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-2 (induced by source paper "gated mechanism for attention based multimodal sentiment analysis"):
  with members:
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "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"
    - "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-3 (induced by source paper "demand forecasting from spatiotemporal data with graph networks and temporal guided embedding"):
  with members:
    - "predicting urban dispersal events a two stage framework through deep survival analysis on mobility data"
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "taxi demand forecasting a hedge based tessellation strategy for improved accuracy"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "attention is all you need"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks"
    - "modeling long and short term temporal patterns with deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "affective computing for large scale heterogeneous multimedia data a survey"):
  with members:
    - "pdanet polarity consistent deep attention network for fine grained visual emotion regression"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "multi attention recurrent network for human communication comprehension"
    - "a data driven approach to mid level perceptual musical feature modeling"
    - "depechemood a bilingual emotion lexicon built through simple yet powerful techniques"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "looking beyond a clever narrative visual context and attention are primary drivers of affect in video advertisements"

Connected through hyperedge HE-5 (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"
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"

Connected through hyperedge HE-6 (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"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "tensor regression networks"
    - "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-7 (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"
    - "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"

Connected through hyperedge HE-8 (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"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "found in translation learning robust joint representations by cyclic translations between modalities"

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)