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: "multi attention recurrent network for human communication comprehension"
  Abstract: Human face-to-face communication is a complex multimodal signal. We use words (language modality), gestures (vision modality) and changes in tone (acoustic modality) to convey our intentions. Humans easily process and understand face-to-face communication, however, comprehending this form of communication remains a significant challenge for Artificial Intelligence (AI). AI must understand each modality and the interactions between them that shape human communication. In this paper, we present a novel neural architecture for understanding human communication called the Multi-attention Recurrent Network (MARN). The main strength of our model comes from discovering interactions between modalities through time using a neural component called the Multi-attention Block (MAB) and storing them in the hybrid memory of a recurrent component called the Long-short Term Hybrid Memory (LSTHM). We perform extensive comparisons on six publicly available datasets for multimodal sentiment analysis, speaker trait recognition and emotion recognition. MARN shows state-of-the-art performance on all the datasets.

Connected through hyperedge HE-1 (induced by source paper "gated mechanism for attention based multimodal sentiment analysis"):
  with members:
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "memory fusion network for multi view sequential learning"
    - "tensor fusion network for multimodal sentiment analysis"
    - "ruminating reader reasoning with gated multi hop attention"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "affective computing for large scale heterogeneous multimedia data a survey"):
  with members:
    - "pdanet polarity consistent deep attention network for fine grained visual emotion regression"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "a data driven approach to mid level perceptual musical feature modeling"
    - "depechemood a bilingual emotion lexicon built through simple yet powerful techniques"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "looking beyond a clever narrative visual context and attention are primary drivers of affect in video advertisements"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"

Connected through hyperedge HE-3 (induced by source paper "incorporating end to end speech recognition models for sentiment analysis"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "on the robustness of speech emotion recognition for human robot interaction with deep neural networks"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "ted lium 3 twice as much data and corpus repartition for experiments on speaker adaptation"
    - "getting the subtext without the text scalable multimodal sentiment classification from visual and acoustic modalities"
    - "reusing neural speech representations for auditory emotion recognition"
    - "cnn lstm architecture for speech emotion recognition with data augmentation"

Connected through hyperedge HE-4 (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"
    - "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-5 (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"
    - "domain adapted word embeddings for improved sentiment classification"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "fault diagnosing slam for varying scale change detection"):
  with members:
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "map management for efficient long term visual localization in outdoor environments"
    - "learning long range perception using self supervision from short range sensors and odometry"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "detection by localization maintenance free change object detector"
    - "lightweight unsupervised deep loop closure"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-7 (induced by source paper "to react or not to react end to end visual pose forecasting for personalized avatar during dyadic conversations"):
  with members:
    - "quaternet a quaternion based recurrent model for human motion"
    - "a face to face neural conversation model"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning to separate object sounds by watching unlabeled video"
    - "deep appearance models for face rendering"
    - "the sound of pixels"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "lattice recurrent unit improving convergence and statistical efficiency for sequence modeling"

Connected through hyperedge HE-8 (induced by source paper "factored latent dynamic conditional random fields for single and multi label sequence modeling"):
  with members:
    - "context model for pedestrian intention prediction using factored latent dynamic conditional random fields"
    - "embedded state latent conditional random fields for sequence labeling"
    - "multi task learning for sequence tagging an empirical study"
    - "video based sign language recognition without temporal segmentation"
    - "lstm a search space odyssey"
    - "joint learning of correlated sequence labelling tasks using bidirectional recurrent neural networks"
    - "hcp a flexible cnn framework for multi label image classification"
    - "cnn rnn a unified framework for multi label image classification"

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)