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: "video action recognition via neural architecture searching"
  Abstract: Deep neural networks have achieved great success for video analysis and understanding. However, designing a high-performance neural architecture requires substantial efforts and expertise. In this paper, we make the first attempt to let algorithm automatically design neural networks for video action recognition tasks. Specifically, a spatio-temporal network is developed in a differentiable space modeled by a directed acyclic graph, thus a gradient-based strategy can be performed to search an optimal architecture. Nonetheless, it is computationally expensive, since the computational burden to evaluate each architecture candidate is still heavy. To alleviate this issue, we, for the video input, introduce a temporal segment approach to reduce the computational cost without losing global video information. For the architecture, we explore in an efficient search space by introducing pseudo 3D operators. Experiments show that, our architecture outperforms popular neural architectures, under the training from scratch protocol, on the challenging UCF101 dataset, surprisingly, with only around one percentage of parameters of its manual-design counterparts.

Connected through hyperedge HE-1 (induced by source paper "searching central difference convolutional networks for face anti spoofing"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "deep pixel wise binary supervision for face presentation attack detection"
    - "deep tree learning for zero shot face anti spoofing"
    - "neural architecture search for deep face recognition"
    - "searching for a robust neural architecture in four gpu hours"
    - "customizable architecture search for semantic segmentation"
    - "auto reid searching for a part aware convnet for person re identification"
    - "gabor convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "rethinking motion representation residual frames with 3d convnets for better action recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "video classification with channel separated convolutional networks"
    - "temporal bilinear networks for video action recognition"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"
    - "tsm temporal shift module for efficient video understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"

Connected through hyperedge HE-3 (induced by source paper "nass optimizing secure inference via neural architecture search"):
  with members:
    - "hardware software co exploration of neural architectures"
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "on neural architecture search for resource constrained hardware platforms"
    - "device circuit architecture co exploration for computing in memory neural accelerators"
    - "xonn xnor based oblivious deep neural network inference"
    - "prada protecting against dnn model stealing attacks"
    - "gazelle a low latency framework for secure neural network inference"

Connected through hyperedge HE-4 (induced by source paper "adaptive and iteratively improving recurrent lateral connections"):
  with members:
    - "graph hypernetworks for neural architecture search"
    - "self supervised spatiotemporal feature learning via video rotation prediction"
    - "spatio temporal channel correlation networks for action classification"
    - "iamnn iterative and adaptive mobile neural network for efficient image classification"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "densepose dense human pose estimation in the wild"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-5 (induced by source paper "learning graph convolutional network for skeleton based human action recognition by neural searching"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "optimized skeleton based action recognition via sparsified graph regression"
    - "darts differentiable architecture search"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"

Connected through hyperedge HE-6 (induced by source paper "device circuit architecture co exploration for computing in memory neural accelerators"):
  with members:
    - "hardware software co exploration of neural architectures"
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "achieving super linear speedup across multi fpga for real time dnn inference"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "on neural architecture search for resource constrained hardware platforms"
    - "architecture search of dynamic cells for semantic video segmentation"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "yolov3 an incremental improvement"

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)