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: "reconstruction free action inference from compressive imagers"
  Abstract: Persistent surveillance from camera networks, such as at parking lots, UAVs, etc., often results in large amounts of video data, resulting in significant challenges for inference in terms of storage, communication and computation. Compressive cameras have emerged as a potential solution to deal with the data deluge issues in such applications. However, inference tasks such as action recognition require high quality features which implies reconstructing the original video data. Much work in compressive sensing (CS) theory is geared towards solving the reconstruction problem, where state-of-the-art methods are computationally intensive and provide low-quality results at high compression rates. Thus, reconstruction-free methods for inference are much desired. In this paper, we propose reconstruction-free methods for action recognition from compressive cameras at high compression ratios of 100 and above. Recognizing actions directly from CS measurements requires features which are mostly nonlinear and thus not easily applicable. This leads us to search for such properties that are preserved in compressive measurements. To this end, we propose the use of spatio-temporal smashed filters, which are compressive domain versions of pixel-domain matched filters. We conduct experiments on publicly available databases and show that one can obtain recognition rates that are comparable to the oracle method in uncompressed setup, even for high compression ratios.

Connected through hyperedge HE-1 (induced by source paper "compressive sensing based privacy for fall detection"):
  with members:
    - "yolov3 an incremental improvement"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep networks for compressed image sensing"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "multilinear compressive learning"):
  with members:
    - "compressed domain image classification using a multi rate neural network"
    - "compressively sensed image recognition"
    - "improving efficiency in convolutional neural networks with multilinear filters"
    - "tensor train recurrent neural networks for video classification"
    - "multilinear class specific discriminant analysis"
    - "cryptodl deep neural networks over encrypted data"
    - "tensor representation in high frequency financial data for price change prediction"
    - "temporal attention augmented bilinear network for financial time series data analysis"

Connected through hyperedge HE-3 (induced by source paper "privacy preserving action recognition using coded aperture videos"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning privacy preserving encodings through adversarial training"
    - "learning to anonymize faces for privacy preserving action detection"
    - "what have we learned from deep representations for action recognition"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "diffusercam lensless single exposure 3d imaging"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "playing for data ground truth from computer games"

Connected through hyperedge HE-4 (induced by source paper "compressed domain image classification using a multi rate neural network"):
  with members:
    - "rate adaptive neural networks for spatial multiplexers"
    - "compressed learning a deep neural network approach"
    - "adam a method for stochastic optimization"
    - "from denoising to compressed sensing"
    - "stable image reconstruction using total variation minimization"

Connected through hyperedge HE-5 (induced by source paper "cs vqa visual question answering with compressively sensed images"):
  with members:
    - "fast integral image estimation at 1 measurement rate"
    - "deep fully connected networks for video compressive sensing"
    - "hierarchical question image co attention for visual question answering"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "a deep learning approach to structured signal recovery"
    - "deep residual learning for image recognition"
    - "ask attend and answer exploring question guided spatial attention for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "compressive light field reconstructions using deep learning"):
  with members:
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "deep fully connected networks for video compressive sensing"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "learning based view synthesis for light field cameras"
    - "context encoders feature learning by inpainting"
    - "learning sensor multiplexing design through back propagation"

Connected through hyperedge HE-7 (induced by source paper "compressively sensed image recognition"):
  with members:
    - "convolutional neural networks for non iterative reconstruction of compressively sensed images"
    - "compressed learning a deep neural network approach"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "lensless photography with only an image sensor"):
  with members:
    - "multi view in lensless compressive imaging"

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)