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: "bag of visual words and fusion methods for action recognition comprehensive study and good practice"
  Abstract: Video based action recognition is one of the important and challenging problems in computer vision research. Bag of Visual Words model (BoVW) with local features has become the most popular method and obtained the state-of-the-art performance on several realistic datasets, such as the HMDB51, UCF50, and UCF101. BoVW is a general pipeline to construct a global representation from a set of local features, which is mainly composed of five steps: (i) feature extraction, (ii) feature pre-processing, (iii) codebook generation, (iv) feature encoding, and (v) pooling and normalization. Many efforts have been made in each step independently in different scenarios and their effect on action recognition is still unknown. Meanwhile, video data exhibits different views of visual pattern, such as static appearance and motion dynamics. Multiple descriptors are usually extracted to represent these different views. Many feature fusion methods have been developed in other areas and their influence on action recognition has never been investigated before. This paper aims to provide a comprehensive study of all steps in BoVW and different fusion methods, and uncover some good practice to produce a state-of-the-art action recognition system. Specifically, we explore two kinds of local features, ten kinds of encoding methods, eight kinds of pooling and normalization strategies, and three kinds of fusion methods. We conclude that every step is crucial for contributing to the final recognition rate. Furthermore, based on our comprehensive study, we propose a simple yet effective representation, called hybrid representation, by exploring the complementarity of different BoVW frameworks and local descriptors. Using this representation, we obtain the state-of-the-art on the three challenging datasets: HMDB51 (61.1%), UCF50 (92.3%), and UCF101 (87.9%).

Connected through hyperedge HE-1 (induced by source paper "avd adversarial video distillation"):
  with members:
    - "deep discriminative model for video classification"
    - "video representation learning using discriminative pooling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "temporal segment networks for action recognition in videos"
    - "the kinetics human action video dataset"
    - "convnet architecture search for spatiotemporal feature learning"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-2 (induced by source paper "improving bag of visual words towards effective facial expressive image classification"):
  with members:
    - "spontaneous facial expression recognition using sparse representation"

Connected through hyperedge HE-3 (induced by source paper "second order temporal pooling for action recognition"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "non linear temporal subspace representations for activity recognition"
    - "video representation learning using discriminative pooling"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-4 (induced by source paper "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"):
  with members:
    - "large scale isolated gesture recognition using convolutional neural networks"
    - "rank pooling for action recognition"
    - "joint distance maps based action recognition with convolutional neural networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "large scale continuous gesture recognition using convolutional neural networks"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "action recognition based on joint trajectory maps using convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep local video feature for action recognition"):
  with members:
    - "deep quantization encoding convolutional activations with deep generative model"
    - "temporal segment networks towards good practices for deep action recognition"
    - "deep temporal linear encoding networks"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "towards good practices for very deep two stream convnets"
    - "modeling spatial temporal clues in a hybrid deep learning framework for video classification"
    - "efficient large scale video classification"
    - "action recognition with trajectory pooled deep convolutional descriptors"

Connected through hyperedge HE-6 (induced by source paper "multi level recurrent residual networks for action recognition"):
  with members:
    - "simple recurrent units for highly parallelizable recurrence"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "convolutional two stream network fusion for video action recognition"
    - "rnn fisher vectors for action recognition and image annotation"
    - "a critical review of recurrent neural networks for sequence learning"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-7 (induced by source paper "two stream lstm a deep fusion framework for human action recognition"):
  with members:
    - "action recognition with image based cnn features"
    - "action recognition using visual attention"
    - "learning to track for spatio temporal action localization"
    - "multi object classification and unsupervised scene understanding using deep learning features and latent tree probabilistic models"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "finding action tubes"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-8 (induced by source paper "convolutional two stream network fusion for video action recognition"):
  with members:
    - "unsupervised learning of video representations using lstms"
    - "human action recognition using factorized spatio temporal convolutional networks"
    - "bilinear cnn models for fine grained visual recognition"
    - "p cnn pose based cnn features for action recognition"
    - "beyond short snippets deep networks for video classification"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "action conditional video prediction using deep networks in atari games"

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)