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: "multi fiber networks for video recognition"
  Abstract: In this paper, we aim to reduce the computational cost of spatio-temporal deep neural networks, making them run as fast as their 2D counterparts while preserving state-of-the-art accuracy on video recognition benchmarks. To this end, we present the novel Multi-Fiber architecture that slices a complex neural network into an ensemble of lightweight networks or fibers that run through the network. To facilitate information flow between fibers we further incorporate multiplexer modules and end up with an architecture that reduces the computational cost of 3D networks by an order of magnitude, while increasing recognition performance at the same time. Extensive experimental results show that our multi-fiber architecture significantly boosts the efficiency of existing convolution networks for both image and video recognition tasks, achieving state-of-the-art performance on UCF-101, HMDB-51 and Kinetics datasets. Our proposed model requires over 9x and 13x less computations than the I3D and R(2+1)D models, respectively, yet providing higher accuracy.

Connected through hyperedge HE-1 (induced by source paper "semantic image networks for human action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "spatio temporal channel correlation networks for action classification"
    - "a 2 nets double attention networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "appearance and relation networks for video classification"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-2 (induced by source paper "dynamic inference a new approach toward efficient video action recognition"):
  with members:
    - "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition"
    - "eco efficient convolutional network for online video understanding"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "motion feature network fixed motion filter for action recognition"
    - "tsm temporal shift module for efficient video understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"

Connected through hyperedge HE-3 (induced by source paper "fine grained instance level sketch based video retrieval"):
  with members:
    - "deep learning for free hand sketch a survey"
    - "deep self supervised representation learning for free hand sketch"
    - "livesketch query perturbations for guided sketch based visual search"
    - "deep zero shot learning for scene sketch"
    - "learning deep sketch abstraction"
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "spatio temporal channel correlation networks for action classification"

Connected through hyperedge HE-4 (induced by source paper "learning class regularized features for action recognition"):
  with members:
    - "class feature pyramids for video explanation"
    - "visualizing and understanding generative adversarial networks extended abstract"
    - "saliency tubes visual explanations for spatio temporal convolutions"
    - "large scale weakly supervised pre training for video action recognition"
    - "explaining explanations an overview of interpretability of machine learning"
    - "analyzing human human interactions a survey"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-5 (induced by source paper "ctm collaborative temporal modeling for action recognition"):
  with members:
    - "stm spatiotemporal and motion encoding for action recognition"
    - "video classification with channel separated convolutional networks"
    - "learning spatio temporal representation with local and global diffusion"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "eco efficient convolutional network for online video understanding"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "a short note about kinetics 600"
    - "tsm temporal shift module for efficient video understanding"

Connected through hyperedge HE-6 (induced by source paper "iqiyi submission to activitynet challenge 2019 kinetics 700 challenge hierarchical group wise attention"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "slowfast networks for video recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-7 (induced by source paper "listen to look action recognition by previewing audio"):
  with members:
    - "what makes training multi modal networks hard"
    - "learning to localize sound sources in visual scenes analysis and applications"
    - "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition"
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "distinit learning video representations without a single labeled video"
    - "co separating sounds of visual objects"
    - "videobert a joint model for video and language representation learning"
    - "temporal cycle consistency learning"

Connected through hyperedge HE-8 (induced by source paper "faster recurrent networks for efficient video classification"):
  with members:
    - "efficient video classification using fewer frames"
    - "scsampler sampling salient clips from video for efficient action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "adaframe adaptive frame selection for fast video recognition"
    - "videos as space time region graphs"
    - "spatio temporal channel correlation networks for action classification"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

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)