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: "multimodal sentiment analysis using hierarchical fusion with context modeling"
  Abstract: Multimodal sentiment analysis is a very actively growing field of research. A promising area of opportunity in this field is to improve the multimodal fusion mechanism. We present a novel feature fusion strategy that proceeds in a hierarchical fashion, first fusing the modalities two in two and only then fusing all three modalities. On multimodal sentiment analysis of individual utterances, our strategy outperforms conventional concatenation of features by 1%, which amounts to 5% reduction in error rate. On utterance-level multimodal sentiment analysis of multi-utterance video clips, for which current state-of-the-art techniques incorporate contextual information from other utterances of the same clip, our hierarchical fusion gives up to 2.4% (almost 10% error rate reduction) over currently used concatenation. The implementation of our method is publicly available in the form of open-source code.

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 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"
    - "efficient low rank multimodal fusion with modality specific factors"

Connected through hyperedge HE-2 (induced by source paper "audio text sentiment analysis using deep robust complementary fusion of multi features and multi modalities"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "deep learning for sentiment analysis a survey"
    - "convolutional attention networks for multimodal emotion recognition from speech and text data"
    - "multimodal speech emotion recognition using audio and text"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multimodal machine learning a survey and taxonomy"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"

Connected through hyperedge HE-3 (induced by source paper "multimodal and multi view models for emotion recognition"):
  with members:
    - "deep contextualized word representations"
    - "speech emotion recognition via contrastive loss under siamese networks"
    - "tensor fusion network for multimodal sentiment analysis"
    - "multi view recurrent neural acoustic word embeddings"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "a survey on multi view learning"

Connected through hyperedge HE-4 (induced by source paper "the omg empathy dataset evaluating the impact of affective behavior in storytelling"):
  with members:
    - "the omg emotion behavior dataset"
    - "multimodal emotion recognition for one minute gradual emotion challenge"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "soundnet learning sound representations from unlabeled video"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "meld a multimodal multi party dataset for emotion recognition in conversations"):
  with members:
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "emotionlines an emotion corpus of multi party conversations"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "tensor fusion network for multimodal sentiment analysis"
    - "a persona based neural conversation model"
    - "convolutional experts constrained local model for facial landmark detection"

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)