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: "truly multi modal youtube 8m video classification with video audio and text"
  Abstract: The YouTube-8M video classification challenge requires teams to classify 0.7 million videos into one or more of 4,716 classes. In this Kaggle competition, we placed in the top 3% out of 650 participants using released video and audio features. Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text. The newly introduced text data is termed as YouTube-8M-Text. We present a classification framework for the joint use of text, visual and audio features, and conduct an extensive set of experiments to quantify the benefit that this additional mode brings. The inclusion of text yields state-of-the-art results, e.g. 86.7% GAP on the YouTube-8M-Text validation dataset.

Connected through hyperedge HE-1 (induced by source paper "randomnet towards fully automatic neural architecture design for multimodal learning"):
  with members:
    - "mfas multimodal fusion architecture search"
    - "best practices for scientific research on neural architecture search"
    - "streaming end to end speech recognition for mobile devices"
    - "centralnet a multilayer approach for multimodal fusion"
    - "efficient neural architecture search via parameter sharing"
    - "learning transferable architectures for scalable image recognition"
    - "efficient architecture search by network transformation"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "learning to separate object sounds by watching unlabeled video"):
  with members:
    - "the conversation deep audio visual speech enhancement"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "the sound of pixels"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "audio visual scene analysis with self supervised multisensory features"
    - "learning word like units from joint audio visual analysis"
    - "objects that sound"

Connected through hyperedge HE-3 (induced by source paper "deep multimodal learning an effective method for video classification"):
  with members:
    - "multimodal machine learning a survey and taxonomy"
    - "learnable pooling with context gating for video classification"
    - "the monkeytyping solution to the youtube 8m video understanding challenge"
    - "the youtube 8m kaggle competition challenges and methods"
    - "temporal modeling approaches for large scale youtube 8m video understanding"
    - "youtube 8m a large scale video classification benchmark"
    - "beyond short snippets deep networks for video classification"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-4 (induced by source paper "centralnet a multilayer approach for multimodal fusion"):
  with members:
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "efficient large scale multi modal classification"
    - "multimodal machine learning a survey and taxonomy"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "youtube 8m a large scale video classification benchmark"
    - "correlational neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "moddrop adaptive multi modal gesture recognition"

Connected through hyperedge HE-5 (induced by source paper "end to end video classification with knowledge graphs"):
  with members:
    - "aggregating frame level features for large scale video classification"
    - "learnable pooling with context gating for video classification"
    - "node2vec scalable feature learning for networks"
    - "youtube 8m a large scale video classification benchmark"
    - "visual relationship detection with language priors"
    - "yfcc100m the new data in multimedia research"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "multimodal visual concept learning with weakly supervised techniques"):
  with members:
    - "learning from video and text via large scale discriminative clustering"
    - "weakly supervised learning of heterogeneous concepts in videos"
    - "title generation for user generated videos"
    - "a dataset for movie description"
    - "using descriptive video services to create a large data source for video annotation research"
    - "weakly supervised alignment of video with text"
    - "coherent multi sentence video description with variable level of detail"
    - "learning spatiotemporal features with 3d convolutional networks"

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.CV(Computer Vision and Pattern Recognition)