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: "look listen and learn a multimodal lstm for speaker identification"
  Abstract: Speaker identification refers to the task of localizing the face of a person who has the same identity as the ongoing voice in a video. This task not only requires collective perception over both visual and auditory signals, the robustness to handle severe quality degradations and unconstrained content variations are also indispensable. In this paper, we describe a novel multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies both visual and auditory modalities from the beginning of each sequence input. The key idea is to extend the conventional LSTM by not only sharing weights across time steps, but also sharing weights across modalities. We show that modeling the temporal dependency across face and voice can significantly improve the robustness to content quality degradations and variations. We also found that our multimodal LSTM is robustness to distractors, namely the non-speaking identities. We applied our multimodal LSTM to The Big Bang Theory dataset and showed that our system outperforms the state-of-the-art systems in speaker identification with lower false alarm rate and higher recognition accuracy.

Connected through hyperedge HE-1 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "online action detection in untrimmed streaming videos modeling and evaluation"
    - "learning to drive in a day"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "lstm a search space odyssey"

Connected through hyperedge HE-2 (induced by source paper "video cloze procedure for self supervised spatio temporal learning"):
  with members:
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "learning image representations by completing damaged jigsaw puzzles"
    - "unsupervised representation learning by predicting image rotations"
    - "self supervised video representation learning with space time cubic puzzles"
    - "transitive invariance for self supervised visual representation learning"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "colorization as a proxy task for visual understanding"

Connected through hyperedge HE-3 (induced by source paper "bio inspired modality fusion for active speaker detection"):
  with members:
    - "ava activespeaker an audio visual dataset for active speaker detection"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "self supervised vision based detection of the active speaker as a prerequisite for socially aware language acquisition"
    - "cross modal supervision for learning active speaker detection in video"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "two phase hair image synthesis by self enhancing generative model"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "faceshop deep sketch based face image editing"
    - "learning dual convolutional neural networks for low level vision"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "hairnet single view hair reconstruction using convolutional neural networks"
    - "residual dense network for image super resolution"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "mganet a robust model for quality enhancement of compressed video"

Connected through hyperedge HE-5 (induced by source paper "gif2video color dequantization and temporal interpolation of gif images"):
  with members:
    - "identity preserving face completion for large ocular region occlusion"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "siclope silhouette based clothed people"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "video frame interpolation via adaptive convolution"
    - "video frame interpolation via adaptive separable convolution"
    - "video frame synthesis using deep voxel flow"

Connected through hyperedge HE-6 (induced by source paper "modeling default rate in p2p lending via lstm"):
  with members:
    - "deep learning for time series analysis"
    - "a recurrent latent variable model for sequential data"

Connected through hyperedge HE-7 (induced by source paper "mganet a robust model for quality enhancement of compressed video"):
  with members:
    - "multi frame quality enhancement for compressed video"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "detail revealing deep video super resolution"
    - "memnet a persistent memory network for image restoration"
    - "video frame synthesis using deep voxel flow"

Connected through hyperedge HE-8 (induced by source paper "a hybrid method for traffic flow forecasting using multimodal deep learning"):
  with members:
    - "spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "deep visual semantic alignments for generating image descriptions"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)