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: "feature sampling and partitioning for visual vocabulary generation on large action classification datasets"
  Abstract: The recent trend in action recognition is towards larger datasets, an increasing number of action classes and larger visual vocabularies. State-of-the-art human action classification in challenging video data is currently based on a bag-of-visual-words pipeline in which space-time features are aggregated globally to form a histogram. The strategies chosen to sample features and construct a visual vocabulary are critical to performance, in fact often dominating performance. In this work we provide a critical evaluation of various approaches to building a vocabulary and show that good practises do have a significant impact. By subsampling and partitioning features strategically, we are able to achieve state-of-the-art results on 5 major action recognition datasets using relatively small visual vocabularies.

Connected through hyperedge HE-1 (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-2 (induced by source paper "pooling the convolutional layers in deep convnets for action recognition"):
  with members:
    - "beyond short snippets deep networks for video classification"
    - "towards good practices for very deep two stream convnets"
    - "exploiting local features from deep networks for image retrieval"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "bag of visual words and fusion methods for action recognition comprehensive study and good practice"
    - "a discriminative cnn video representation for event detection"

Connected through hyperedge HE-3 (induced by source paper "handcrafted local features are convolutional neural networks"):
  with members:
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "unsupervised learning of video representations using lstms"
    - "beyond short snippets deep networks for video classification"
    - "modeling spatial temporal clues in a hybrid deep learning framework for video classification"
    - "the best of both worlds combining data independent and data driven approaches for action recognition"
    - "efficient large scale video classification"
    - "bag of visual words and fusion methods for action recognition comprehensive study and good practice"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "the best of both worlds combining data independent and data driven approaches for action recognition"):
  with members:
    - "unsupervised learning of video representations using lstms"
    - "beyond short snippets deep networks for video classification"
    - "bag of visual words and fusion methods for action recognition comprehensive study and good practice"
    - "video language modeling a baseline for generative models of natural videos"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "beyond gaussian pyramid multi skip feature stacking for action recognition"
    - "two stream convolutional networks for action recognition in videos"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "improving human activity recognition through ranking and re ranking"):
  with members:
    - "webly supervised learning of convolutional networks"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "a discriminative cnn video representation for event detection"
    - "beyond gaussian pyramid multi skip feature stacking for action recognition"

Connected through hyperedge HE-6 (induced by source paper "feature sampling strategies for action recognition"):
  with members:
    - "actionness estimation using hybrid fully convolutional networks"
    - "finding action tubes"
    - "how good are detection proposals really"

Connected through hyperedge HE-7 (induced by source paper "beyond spatial pyramid matching space time extended descriptor for action recognition"):
  with members:
    - "bag of visual words and fusion methods for action recognition comprehensive study and good practice"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-8 (induced by source paper "long short term motion feature for action classification and retrieval"):
  with members:
    - "bag of visual words and fusion methods for action recognition comprehensive study and good practice"
    - "two stream convolutional networks for action recognition in videos"

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)