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: "investigating audio visual and text fusion methods for end to end automatic personality prediction"
  Abstract: We propose a tri-modal architecture to predict Big Five personality trait scores from video clips with different channels for audio, text, and video data. For each channel, stacked Convolutional Neural Networks are employed. The channels are fused both on decision-level and by concatenating their respective fully connected layers. It is shown that a multimodal fusion approach outperforms each single modality channel, with an improvement of 9.4\% over the best individual modality (video). Full backpropagation is also shown to be better than a linear combination of modalities, meaning complex interactions between modalities can be leveraged to build better models. Furthermore, we can see the prediction relevance of each modality for each trait. The described model can be used to increase the emotional intelligence of virtual agents.

Connected through hyperedge HE-1 (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"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "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-2 (induced by source paper "recent trends in deep learning based personality detection"):
  with members:
    - "sentiment and sarcasm classification with multitask learning"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "bi modal first impressions recognition using temporally ordered deep audio and stochastic visual features"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "deep residual learning for image recognition"
    - "pr2 a language independent unsupervised tool for personality recognition from text"

Connected through hyperedge HE-3 (induced by source paper "on the effect of observed subject biases in apparent personality analysis from audio visual signals"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"
    - "age progression regression by conditional adversarial autoencoder"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "bi modal first impressions recognition using temporally ordered deep audio and stochastic visual features"
    - "deep impression audiovisual deep residual networks for multimodal apparent personality trait recognition"

Connected through hyperedge HE-4 (induced by source paper "myers briggs personality classification and personality specific language generation using pre trained language models"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "modality based factorization for multimodal fusion"):
  with members:
    - "efficient low rank multimodal fusion with modality specific factors"
    - "tensor fusion network for multimodal sentiment analysis"
    - "multimodal machine learning a survey and taxonomy"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "adam a method for stochastic optimization"
    - "medical image fusion a survey of the state of the art"

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.AI(Artificial Intelligence)