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: "modality based factorization for multimodal fusion"
  Abstract: We propose a multimodal data fusion method by obtaining a $M+1$ dimensional tensor to consider the high-order relationship between $M$ modalities and the output layer of a neural network model. Applying a modality-based tensor factorization method, which adopts different factors for different modalities, results in removing the redundant information with respect to model outputs and leads to fewer model parameters with minimal loss of performance. #R##N#This factorization method works as a regularizer which leads to a less complicated model and avoids overfitting. In addition, a modality-based factorization approach helps to understand the amount of useful information in each modality. #R##N#We have applied this method to three different multimodal datasets in sentiment analysis, personality trait recognition, and emotion recognition. The results demonstrate that the approach yields a 1\% to 4\% improvement on several evaluation measures compared to the state-of-the-art for all three tasks.

Connected through hyperedge HE-1 (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"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "words can shift dynamically adjusting word representations using nonverbal behaviors"
    - "learning factorized multimodal representations"

Connected through hyperedge HE-2 (induced by source paper "m bert injecting multimodal information in the bert structure"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "deep contextualized word representations"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "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.LG(Machine Learning)