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: "select additive learning improving cross individual generalization in multimodal sentiment analysis"
  Abstract: Multimodal sentiment analysis is drawing an increasing amount of attention these days. It enables mining of opinions in video reviews and surveys which are now available aplenty on online platforms like YouTube. However, the limited number of high-quality multimodal sentiment data samples may introduce the problem of the sentiment being dependent on the individual specific features in the dataset. This results in a lack of generalizability of the trained models for classification on larger online platforms. In this paper, we first examine the data and verify the existence of this dependence problem. Then we propose a Select-Additive Learning (SAL) procedure that improves the generalizability of trained discriminative neural networks. SAL is a two-phase learning method. In Selection phase, it selects the confounding learned representation. In Addition phase, it forces the classifier to discard confounded representations by adding Gaussian noise. In our experiments, we show how SAL improves the generalizability of state-of-the-art models. We increase prediction accuracy significantly in all three modalities (text, audio, video), as well as in their fusion. We show how SAL, even when trained on one dataset, achieves good accuracy across test datasets.

Connected through hyperedge HE-1 (induced by source paper "multimodal sentiment analysis with word level fusion and reinforcement learning"):
  with members:
    - "multimodal machine learning a survey and taxonomy"
    - "recent trends in deep learning based natural language processing"
    - "image captioning with semantic attention"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "neural architecture search with reinforcement learning"
    - "convolutional experts constrained local model for facial landmark detection"
    - "select additive learning improving generalization in multimodal sentiment analysis"

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)