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: "deep local video feature for action recognition"
  Abstract: We investigate the problem of representing an entire video using CNN features for human action recognition. Currently, limited by GPU memory, we have not been able to feed a whole video into CNN/RNNs for end-to-end learning. A common practice is to use sampled frames as inputs and video labels as supervision. One major problem of this popular approach is that the local samples may not contain the information indicated by global labels. To deal with this problem, we propose to treat the deep networks trained on local inputs as local feature extractors. After extracting local features, we aggregate them into global features and train another mapping function on the same training data to map the global features into global labels. We study a set of problems regarding this new type of local features such as how to aggregate them into global features. Experimental results on HMDB51 and UCF101 datasets show that, for these new local features, a simple maximum pooling on the sparsely sampled features lead to significant performance improvement.

Connected through hyperedge HE-1 (induced by source paper "towards universal representation for unseen action recognition"):
  with members:
    - "deep texture manifold for ground terrain recognition"
    - "alternative semantic representations for zero shot human action recognition"
    - "hidden two stream convolutional networks for action recognition"
    - "densenet for dense flow"
    - "the kinetics human action video dataset"
    - "guided optical flow learning"
    - "image de raining using a conditional generative adversarial network"
    - "spatial aware object embeddings for zero shot localization and classification of actions"

Connected through hyperedge HE-2 (induced by source paper "gated transfer network for transfer learning"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "deep texture manifold for ground terrain recognition"
    - "towards universal representation for unseen action recognition"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "hidden two stream convolutional networks for action recognition"
    - "squeeze and excitation networks"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"

Connected through hyperedge HE-3 (induced by source paper "random temporal skipping for multirate video analysis"):
  with members:
    - "deep texture manifold for ground terrain recognition"
    - "learning optical flow via dilated networks and occlusion reasoning"
    - "towards universal representation for unseen action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "hidden two stream convolutional networks for action recognition"
    - "densenet for dense flow"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-4 (induced by source paper "real time action recognition with dissimilarity based training of specialized module networks"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "going deeper into action recognition a survey"
    - "convolutional two stream network fusion for video action recognition"
    - "deep residual learning for image recognition"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-5 (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"
    - "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"
    - "deep quantization encoding convolutional activations with deep generative model"

Connected through hyperedge HE-6 (induced by source paper "hidden two stream convolutional networks for action recognition"):
  with members:
    - "video enhancement with task oriented flow"
    - "deep texture manifold for ground terrain recognition"
    - "learning optical flow via dilated networks and occlusion reasoning"
    - "towards universal representation for unseen action recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "compressed video action recognition"

Connected through hyperedge HE-7 (induced by source paper "large scale mapping of human activity using geo tagged videos"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "hidden two stream convolutional networks for action recognition"
    - "densenet for dense flow"
    - "guided optical flow learning"
    - "spatio temporal sentiment hotspot detection using geotagged photos"
    - "depth2action exploring embedded depth for large scale action recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "youtube 8m a large scale video classification benchmark"

Connected through hyperedge HE-8 (induced by source paper "video representation learning and latent concept mining for large scale multi label video classification"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "deep quantization encoding convolutional activations with deep generative model"
    - "temporal segment networks towards good practices for deep action recognition"
    - "youtube 8m a large scale video classification benchmark"
    - "feature pyramid networks for object detection"
    - "videolstm convolves attends and flows for action recognition"
    - "zoneout regularizing rnns by randomly preserving hidden activations"

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)