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: "fusing multi stream deep networks for video classification"
  Abstract: This paper studies deep network architectures to address the problem of video classification. A multi-stream framework is proposed to fully utilize the rich multimodal information in videos. Specifically, we first train three Convolutional Neural Networks to model spatial, short-term motion and audio clues respectively. Long Short Term Memory networks are then adopted to explore long-term temporal dynamics. With the outputs of the individual streams, we propose a simple and effective fusion method to generate the final predictions, where the optimal fusion weights are learned adaptively for each class, and the learning process is regularized by automatically estimated class relationships. Our contributions are two-fold. First, the proposed multi-stream framework is able to exploit multimodal features that are more comprehensive than those previously attempted. Second, we demonstrate that the adaptive fusion method using the class relationship as a regularizer outperforms traditional alternatives that estimate the weights in a "free" fashion. Our framework produces significantly better results than the state of the arts on two popular benchmarks, 92.2\% on UCF-101 (without using audio) and 84.9\% on Columbia Consumer Videos.

Connected through hyperedge HE-1 (induced by source paper "mod a deep mixture model with online knowledge distillation for large scale video temporal concept localization"):
  with members:
    - "non local netvlad encoding for video classification"
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "knowledge distillation by on the fly native ensemble"
    - "large scale distributed neural network training through online distillation"
    - "deep mutual learning"
    - "learning without forgetting"
    - "convolutional two stream network fusion for video action recognition"
    - "delving deeper into convolutional networks for learning video representations"

Connected through hyperedge HE-2 (induced by source paper "motion feature network fixed motion filter for action recognition"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "shift a zero flop zero parameter alternative to spatial convolutions"
    - "appearance and relation networks for video classification"
    - "learnable pooling with context gating for video classification"
    - "hidden two stream convolutional networks for action recognition"
    - "the kinetics human action video dataset"
    - "convnet architecture search for spatiotemporal feature learning"

Connected through hyperedge HE-3 (induced by source paper "discrimnet semi supervised action recognition from videos using generative adversarial networks"):
  with members:
    - "slow feature analysis for human action recognition"
    - "unsupervised representation learning by sorting sequences"
    - "nips 2016 tutorial generative adversarial networks"
    - "deep networks with stochastic depth"
    - "improved techniques for training gans"
    - "adversarial feature learning"
    - "self supervised video representation learning with odd one out networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"):
  with members:
    - "knowledge distillation by on the fly native ensemble"
    - "deep mutual learning"
    - "learnable pooling with context gating for video classification"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "squeeze and excitation networks"
    - "aggregated residual transformations for deep neural networks"
    - "cnn architectures for large scale audio classification"
    - "learning without forgetting"

Connected through hyperedge HE-5 (induced by source paper "object level context modeling for scene classification with context cnn"):
  with members:
    - "locally supervised deep hybrid model for scene recognition"
    - "semantic object parsing with graph lstm"
    - "cnn rnn a unified framework for multi label image classification"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "exploring models and data for image question answering"
    - "semantic object parsing with local global long short term memory"
    - "vqa visual question answering"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "chained multi stream networks exploiting pose motion and appearance for action classification and detection"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "improved dense trajectory with cross streams"
    - "orientation boosted voxel nets for 3d object recognition"
    - "actionness estimation using hybrid fully convolutional networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "sympathy for the details dense trajectories and hybrid classification architectures for action recognition"

Connected through hyperedge HE-7 (induced by source paper "zero shot visual recognition via bidirectional latent embedding"):
  with members:
    - "learning deep representations of fine grained visual descriptions"
    - "label embedding for image classification"
    - "synthesized classifiers for zero shot learning"
    - "multi label zero shot learning via concept embedding"
    - "temporal segment networks towards good practices for deep action recognition"
    - "predicting visual exemplars of unseen classes for zero shot learning"
    - "latent embeddings for zero shot classification"
    - "learning attributes equals multi source domain generalization"

Connected through hyperedge HE-8 (induced by source paper "sympathy for the details dense trajectories and hybrid classification architectures for action recognition"):
  with members:
    - "convolutional two stream network fusion for video action recognition"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "unsupervised learning of video representations using lstms"
    - "delving deeper into convolutional networks for learning video representations"
    - "human action recognition using factorized spatio temporal convolutional networks"
    - "hyper fisher vectors for action recognition"
    - "a robust and efficient video representation for action recognition"
    - "beyond short snippets deep networks for video 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.CV(Computer Vision and Pattern Recognition)