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: "representation flow for action recognition"
  Abstract: In this paper, we propose a convolutional layer inspired by optical flow algorithms to learn motion representations. Our representation flow layer is a fully-differentiable layer designed to optimally capture the `flow' of any representation channel within a convolutional neural network. Its parameters for iterative flow optimization are learned in an end-to-end fashion together with the other model parameters, maximizing the action recognition performance. Furthermore, we newly introduce the concept of learning `flow of flow' representations by stacking multiple representation flow layers. We conducted extensive experimental evaluations, confirming its advantages over previous recognition models using traditional optical flows in both computational speed and performance.

Connected through hyperedge HE-1 (induced by source paper "lightweight network architecture for real time action recognition"):
  with members:
    - "universal transformers"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "massively parallel video networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning efficient convolutional networks through network slimming"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "gate shift networks for video action recognition"):
  with members:
    - "attentive spatio temporal representation learning for diving classification"
    - "video modeling with correlation networks"
    - "action recognition with spatial temporal discriminative filter banks"
    - "stm spatiotemporal and motion encoding for action recognition"
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "saliency tubes visual explanations for spatio temporal convolutions"
    - "grouped spatial temporal aggregation for efficient action recognition"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"

Connected through hyperedge HE-3 (induced by source paper "video modeling with correlation networks"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "end to end learning of motion representation for video understanding"
    - "motion feature network fixed motion filter for action recognition"
    - "using phase instead of optical flow for action recognition"
    - "videos as space time region graphs"
    - "spatio temporal channel correlation networks for action classification"
    - "learning discriminative motion features through detection"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-4 (induced by source paper "learning efficient video representation with video shuffle networks"):
  with members:
    - "learning video representations from correspondence proposals"
    - "collaborative spatio temporal feature learning for video action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "end to end learning of motion representation for video understanding"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"
    - "tsm temporal shift module for efficient video understanding"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-5 (induced by source paper "spin a high speed high resolution vision dataset for tracking and action recognition in ping pong"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "dynamonet dynamic action and motion network"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "tracking noisy targets a review of recent object tracking approaches"
    - "a proposal based solution to spatio temporal action detection in untrimmed videos"
    - "video action transformer network"
    - "a 2 nets double attention networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-6 (induced by source paper "dmcl distillation multiple choice learning for multimodal action recognition"):
  with members:
    - "unifying heterogeneous classifiers with distillation"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "learning with privileged information via adversarial discriminative modality distillation"
    - "human action recognition and prediction a survey"
    - "snapshot distillation teacher student optimization in one generation"
    - "action recognition for depth video using multi view dynamic images"
    - "motion feature network fixed motion filter for action recognition"
    - "modality distillation with multiple stream networks for action recognition"

Connected through hyperedge HE-7 (induced by source paper "flow distilled ip two stream networks for compressed video action recognition"):
  with members:
    - "dmc net generating discriminative motion cues for fast compressed video action recognition"
    - "learning with privileged information via adversarial discriminative modality distillation"
    - "eco efficient convolutional network for online video understanding"
    - "end to end learning of motion representation for video understanding"
    - "d3d distilled 3d networks for video action recognition"
    - "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-8 (induced by source paper "tiny video networks"):
  with members:
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "searching for mobilenetv3"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "moments in time dataset one million videos for event understanding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fine grained activity recognition in baseball videos"
    - "multi fiber networks for video recognition"

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)