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: "aenet learning deep audio features for video analysis"
  Abstract: We propose a new deep network for audio event recognition, called AENet. In contrast to speech, sounds coming from audio events may be produced by a wide variety of sources. Furthermore, distinguishing them often requires analyzing an extended time period due to the lack of clear sub-word units that are present in speech. In order to incorporate this long-time frequency structure of audio events, we introduce a convolutional neural network (CNN) operating on a large temporal input. In contrast to previous works this allows us to train an audio event detection system end-to-end. The combination of our network architecture and a novel data augmentation outperforms previous methods for audio event detection by 16%. Furthermore, we perform transfer learning and show that our model learnt generic audio features, similar to the way CNNs learn generic features on vision tasks. In video analysis, combining visual features and traditional audio features such as MFCC typically only leads to marginal improvements. Instead, combining visual features with our AENet features, which can be computed efficiently on a GPU, leads to significant performance improvements on action recognition and video highlight detection. In video highlight detection, our audio features improve the performance by more than 8% over visual features alone.

Connected through hyperedge HE-1 (induced by source paper "autofoley artificial synthesis of synchronized sound tracks for silent videos with deep learning"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "learning sight from sound ambient sound provides supervision for visual learning"
    - "multimodal machine learning a survey and taxonomy"
    - "fast slow recurrent neural networks"
    - "look listen and learn"
    - "video frame interpolation via adaptive separable convolution"
    - "visual to sound generating natural sound for videos in the wild"
    - "video frame synthesis using deep voxel flow"

Connected through hyperedge HE-2 (induced by source paper "summarynet a multi stage deep learning model for automatic video summarisation"):
  with members:
    - "cycle sum cycle consistent adversarial lstm networks for unsupervised video summarization"
    - "video summarization via actionness ranking"
    - "discriminative feature learning for unsupervised video summarization"
    - "video summarization using fully convolutional sequence networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "video summarization with long short term memory"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "audio visual scene aware dialog"):
  with members:
    - "tvqa localized compositional video question answering"
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "a dataset for document grounded conversations"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "dense captioning events in videos"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-4 (induced by source paper "audio visual model distillation using acoustic images"):
  with members:
    - "multi stream network with temporal attention for environmental sound classification"
    - "the conversation deep audio visual speech enhancement"
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual event localization in unconstrained videos"
    - "modality distillation with multiple stream networks for action recognition"
    - "the sound of pixels"
    - "audio visual scene analysis with self supervised multisensory features"

Connected through hyperedge HE-5 (induced by source paper "improving voice separation by incorporating end to end speech recognition"):
  with members:
    - "wham extending speech separation to noisy environments"
    - "recursive speech separation for unknown number of speakers"
    - "the conversation deep audio visual speech enhancement"
    - "espnet end to end speech processing toolkit"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "multi scale multi band densenets for audio source separation"
    - "automatic pronunciation generation by utilizing a semi supervised deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "data augmentation approaches for improving animal audio classification"):
  with members:
    - "deep learning a critical appraisal"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "multi label music genre classification from audio text and images using deep features"
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "places an image database for deep scene understanding"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "cross domain deep feature combination for bird species classification with audio visual data"):
  with members:
    - "impostor networks for fast fine grained recognition"
    - "multimodal machine learning a survey and taxonomy"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "fine grained image classification via combining vision and language"
    - "ambient sound provides supervision for visual learning"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"
    - "character level convolutional networks for text classification"
    - "multimodal convolutional neural networks for matching image and sentence"

Connected through hyperedge HE-8 (induced by source paper "speech driven facial reenactment using conditional generative adversarial networks"):
  with members:
    - "progressive growing of gans for improved quality stability and variation"
    - "mocogan decomposing motion and content for video generation"
    - "semi latent gan learning to generate and modify facial images from attributes"
    - "generating images with recurrent adversarial networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "generative visual manipulation on the natural image manifold"
    - "autoencoding beyond pixels using a learned similarity metric"
    - "adam a method for stochastic optimization"

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.MM(Multimedia)