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: "temporal pyramid pooling based convolutional neural networks for action recognition"
  Abstract: Encouraged by the success of Convolutional Neural Networks (CNNs) in image classification, recently much effort is spent on applying CNNs to video based action recognition problems. One challenge is that video contains a varying number of frames which is incompatible to the standard input format of CNNs. Existing methods handle this issue either by directly sampling a fixed number of frames or bypassing this issue by introducing a 3D convolutional layer which conducts convolution in spatial-temporal domain. #R##N#To solve this issue, here we propose a novel network structure which allows an arbitrary number of frames as the network input. The key of our solution is to introduce a module consisting of an encoding layer and a temporal pyramid pooling layer. The encoding layer maps the activation from previous layers to a feature vector suitable for pooling while the temporal pyramid pooling layer converts multiple frame-level activations into a fixed-length video-level representation. In addition, we adopt a feature concatenation layer which combines appearance information and motion information. Compared with the frame sampling strategy, our method avoids the risk of missing any important frames. Compared with the 3D convolutional method which requires a huge video dataset for network training, our model can be learned on a small target dataset because we can leverage the off-the-shelf image-level CNN for model parameter initialization. Experiments on two challenging datasets, Hollywood2 and HMDB51, demonstrate that our method achieves superior performance over state-of-the-art methods while requiring much fewer training data.

Connected through hyperedge HE-1 (induced by source paper "t recs training for rate invariant embeddings by controlling speed for action recognition"):
  with members:
    - "can recurrent neural networks warp time"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "the kinetics human action video dataset"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-2 (induced by source paper "end to end video level representation learning for action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep local video feature for action recognition"
    - "learning gating convnet for two stream based methods in action recognition"
    - "temporal segment networks for action recognition in videos"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "ts lstm and temporal inception exploiting spatiotemporal dynamics for activity recognition"
    - "eigen evolution pooling for human action recognition"

Connected through hyperedge HE-3 (induced by source paper "evolution preserving dense trajectory descriptors"):
  with members:
    - "temporal segment networks towards good practices for deep action recognition"
    - "deep residual learning for image recognition"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "imagenet large scale visual recognition challenge"
    - "two stream convolutional networks for action recognition in videos"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "person re identification past present and future"):
  with members:
    - "end to end comparative attention networks for person re identification"
    - "how far are we from solving pedestrian detection"
    - "person re identification in the wild"
    - "learning a discriminative null space for person re identification"
    - "disturblabel regularizing cnn on the loss layer"
    - "a survey on learning to hash"
    - "a richly annotated dataset for pedestrian attribute recognition"
    - "deep linear discriminant analysis on fisher networks a hybrid architecture for person re identification"

Connected through hyperedge HE-5 (induced by source paper "every moment counts dense detailed labeling of actions in complex videos"):
  with members:
    - "recognizing fine grained and composite activities using hand centric features and script data"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "unsupervised learning of video representations using lstms"
    - "initialization strategies of spatio temporal convolutional neural networks"
    - "beyond short snippets deep networks for video classification"
    - "show attend and tell neural image caption generation with visual attention"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

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)