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 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 which are now available aplenty on online platforms. However, multimodal sentiment analysis has only a few high-quality data sets annotated for training machine learning algorithms. These limited resources restrict the generalizability of models, where, for example, the unique characteristics of a few speakers (e.g., wearing glasses) may become a confounding factor for the sentiment classification task. In this paper, we propose a Select-Additive Learning (SAL) procedure that improves the generalizability of trained neural networks for multimodal sentiment analysis. In our experiments, we show that our SAL approach improves prediction accuracy significantly in all three modalities (verbal, acoustic, visual), as well as in their fusion. Our results show that SAL, even when trained on one dataset, achieves good generalization across two new test datasets.

Connected through hyperedge HE-1 (induced by source paper "high frequency component helps explain the generalization of convolutional neural networks"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "batch normalization is a cause of adversarial vulnerability"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "learning robust representations by projecting superficial statistics out"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-2 (induced by source paper "learning robust global representations by penalizing local predictive power"):
  with members:
    - "domain adaptation with asymmetrically relaxed distribution alignment"
    - "domain generalization by solving jigsaw puzzles"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "learning robust representations by projecting superficial statistics out"
    - "do imagenet classifiers generalize to imagenet"
    - "best sources forward domain generalization through source specific nets"
    - "domain invariant projection learning for zero shot recognition"
    - "deep visual domain adaptation a survey"

Connected through hyperedge HE-3 (induced by source paper "learning robust representations by projecting superficial statistics out"):
  with members:
    - "best sources forward domain generalization through source specific nets"
    - "invariant representations without adversarial training"
    - "generalizing across domains via cross gradient training"
    - "hallucinating agnostic images to generalize across domains"
    - "domain adaptation for visual applications a comprehensive survey"
    - "learning to generalize meta learning for domain generalization"
    - "measuring the tendency of cnns to learn surface statistical regularities"
    - "domain generalization for object recognition with multi task autoencoders"

Connected through hyperedge HE-4 (induced by source paper "deep learning for sentiment analysis a survey"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "tensor fusion network for multimodal sentiment analysis"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "volatility prediction using financial disclosures sentiments with word embedding based ir models"
    - "deep memory networks for attitude identification"
    - "interactive attention networks for aspect level sentiment classification"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "what if we simply swap the two text fragments a straightforward yet effective way to test the robustness of methods to confounding signals in nature language inference tasks"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "hypothesis only baselines in natural language inference"
    - "deep contextualized word representations"
    - "adversarially regularising neural nli models to integrate logical background knowledge"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "enhancing sentence embedding with generalized pooling"

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 sentiment analysis with word level fusion and reinforcement learning"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "multimodal language analysis with recurrent multistage fusion"):
  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 local global ranking fusion for emotion recognition"
    - "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis"
    - "reusing neural speech representations for auditory emotion recognition"
    - "learning factorized multimodal representations"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "visual features for context aware speech recognition"

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 affective analysis using hierarchical attention strategy with word level alignment"
    - "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"

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)