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: "efficient low rank multimodal fusion with modality specific factors"
  Abstract: Multimodal research is an emerging field of artificial intelligence, and one of the main research problems in this field is multimodal fusion. The fusion of multimodal data is the process of integrating multiple unimodal representations into one compact multimodal representation. Previous research in this field has exploited the expressiveness of tensors for multimodal representation. However, these methods often suffer from exponential increase in dimensions and in computational complexity introduced by transformation of input into tensor. In this paper, we propose the Low-rank Multimodal Fusion method, which performs multimodal fusion using low-rank tensors to improve efficiency. We evaluate our model on three different tasks: multimodal sentiment analysis, speaker trait analysis, and emotion recognition. Our model achieves competitive results on all these tasks while drastically reducing computational complexity. Additional experiments also show that our model can perform robustly for a wide range of low-rank settings, and is indeed much more efficient in both training and inference compared to other methods that utilize tensor representations.

Connected through hyperedge HE-1 (induced by source paper "h owan multi distorted image restoration with tensor 1x1 convolution"):
  with members:
    - "path restore learning network path selection for image restoration"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "attention based adaptive selection of operations for image restoration in the presence of unknown combined distortions"
    - "mutan multimodal tucker fusion for visual question answering"
    - "single image super resolution via a lightweight residual convolutional neural network"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-2 (induced by source paper "what makes training multi modal classification networks hard"):
  with members:
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "baidu uts submission to the epic kitchens action recognition challenge 2019"
    - "scaling egocentric vision the epic kitchens dataset"
    - "hypothesis only baselines in natural language inference"
    - "shifting the baseline single modality performance on visual navigation qa"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "slowfast networks for video recognition"
    - "the sound of pixels"

Connected through hyperedge HE-3 (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-4 (induced by source paper "low rank hoca efficient high order cross modal attention for video captioning"):
  with members:
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "reconstruction network for video captioning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "video captioning via hierarchical reinforcement learning"
    - "weakly supervised dense video captioning"
    - "video captioning with guidance of multimodal latent topics"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "video captioning with transferred semantic attributes"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (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"
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "multi attention recurrent network for human communication comprehension"
    - "domain adapted word embeddings for improved sentiment classification"
    - "words can shift dynamically adjusting word representations using nonverbal behaviors"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "ur funny a multimodal language dataset for understanding humor"):
  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"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "modality based factorization for multimodal fusion"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "words can shift dynamically adjusting word representations using nonverbal behaviors"
    - "learning factorized multimodal representations"

Connected through hyperedge HE-8 (induced by source paper "m bert injecting multimodal information in the bert structure"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "modality based factorization for multimodal fusion"
    - "deep contextualized word representations"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "words can shift dynamically adjusting word representations using nonverbal behaviors"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning factorized multimodal representations"
    - "tensor fusion network for 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.AI(Artificial Intelligence)