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: "learning sensor multiplexing design through back propagation"
  Abstract: Recent progress on many imaging and vision tasks has been driven by the use of deep feed-forward neural networks, which are trained by propagating gradients of a loss defined on the final output, back through the network up to the first layer that operates directly on the image. We propose back-propagating one step further---to learn camera sensor designs jointly with networks that carry out inference on the images they capture. In this paper, we specifically consider the design and inference problems in a typical color camera---where the sensor is able to measure only one color channel at each pixel location, and computational inference is required to reconstruct a full color image. We learn the camera sensor's color multiplexing pattern by encoding it as layer whose learnable weights determine which color channel, from among a fixed set, will be measured at each location. These weights are jointly trained with those of a reconstruction network that operates on the corresponding sensor measurements to produce a full color image. Our network achieves significant improvements in accuracy over the traditional Bayer pattern used in most color cameras. It automatically learns to employ a sparse color measurement approach similar to that of a recent design, and moreover, improves upon that design by learning an optimal layout for these measurements.

Connected through hyperedge HE-1 (induced by source paper "blending diverse physical priors with neural networks"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "neural illumination lighting prediction for indoor environments"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "block coordinate regularization by denoising"
    - "physics based neural networks for shape from polarization"
    - "heavy rain image restoration integrating physics model and conditional adversarial learning"
    - "neural lander stable drone landing control using learned dynamics"
    - "deep learning approach for fourier ptychography microscopy"

Connected through hyperedge HE-2 (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"
    - "a deep learning approach to structured signal recovery"

Connected through hyperedge HE-3 (induced by source paper "convolutional neural networks that teach microscopes how to image"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "deep convolutional neural networks for microscopy based point of care diagnostics"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "asp vision optically computing the first layer of convolutional neural networks using angle sensitive pixels"
    - "pixel recurrent neural networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "why m heads are better than one training a diverse ensemble of deep networks"

Connected through hyperedge HE-4 (induced by source paper "data driven coded aperture design for depth recovery"):
  with members:
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "depth map prediction from a single image using a multi scale deep network"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"
    - "deep convolutional neural fields for depth estimation from a single image"

Connected through hyperedge HE-5 (induced by source paper "deepbinarymask learning a binary mask for video compressive sensing"):
  with members:
    - "rank minimization for snapshot compressive imaging"
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "snapshot compressed sensing performance bounds and algorithms"
    - "deep fully connected networks for video compressive sensing"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "context encoders feature learning by inpainting"

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.LG(Machine Learning)