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: "asp vision optically computing the first layer of convolutional neural networks using angle sensitive pixels"
  Abstract: Deep learning using convolutional neural networks (CNNs) is quickly becoming the state-of-the-art for challenging computer vision applications. However, deep learning's power consumption and bandwidth requirements currently limit its application in embedded and mobile systems with tight energy budgets. In this paper, we explore the energy savings of optically computing the first layer of CNNs. To do so, we utilize bio-inspired Angle Sensitive Pixels (ASPs), custom CMOS diffractive image sensors which act similar to Gabor filter banks in the V1 layer of the human visual cortex. ASPs replace both image sensing and the first layer of a conventional CNN by directly performing optical edge filtering, saving sensing energy, data bandwidth, and CNN FLOPS to compute. Our experimental results (both on synthetic data and a hardware prototype) for a variety of vision tasks such as digit recognition, object recognition, and face identification demonstrate using ASPs while achieving similar performance compared to traditional deep learning pipelines.

Connected through hyperedge HE-1 (induced by source paper "fixynn efficient hardware for mobile computer vision via transfer learning"):
  with members:
    - "fix your classifier the marginal value of training the last weight layer"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"
    - "eva 2 exploiting temporal redundancy in live computer vision"
    - "towards closing the energy gap between hog and cnn features for embedded vision"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning multiple visual domains with residual adapters"

Connected through hyperedge HE-2 (induced by source paper "implementation of optical deep neural networks using the fabry perot interferometer"):
  with members:
    - "large scale optical neural networks based on photoelectric multiplication"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "kronecker recurrent units"
    - "gated orthogonal recurrent units on learning to forget"
    - "the numerics of gans"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "searching for activation functions"
    - "associative long short term memory"

Connected through hyperedge HE-3 (induced by source paper "eva 2 exploiting temporal redundancy in live computer vision"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "flow guided feature aggregation for video object detection"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "video classification with cnns using the codec as a spatio temporal activity sensor"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "reconfiguring the imaging pipeline for computer vision"
    - "exploring computation communication tradeoffs in camera systems"

Connected through hyperedge HE-4 (induced by source paper "euphrates algorithm soc co design for low power mobile continuous vision"):
  with members:
    - "mobile machine learning hardware at arm a systems on chip soc perspective"
    - "in datacenter performance analysis of a tensor processing unit"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"
    - "towards closing the energy gap between hog and cnn features for embedded vision"
    - "video classification with cnns using the codec as a spatio temporal activity sensor"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "reconfiguring the imaging pipeline for computer vision"

Connected through hyperedge HE-5 (induced by source paper "an optical frontend for a convolutional neural network"):
  with members:
    - "analysis of diffractive optical neural networks and their integration with electronic neural networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "cudnn efficient primitives for deep learning"
    - "fast training of convolutional networks through ffts"

Connected through hyperedge HE-6 (induced by source paper "design automation for binarized neural networks a quantum leap opportunity"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "origami a 803 gop s w convolutional network accelerator"
    - "cbinfer change based inference for convolutional neural networks on video data"
    - "finn a framework for fast scalable binarized neural network inference"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "reducing the energy cost of inference via in sensor information processing"
    - "analysis and design of a passive switched capacitor matrix multiplier for approximate computing"

Connected through hyperedge HE-7 (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"
    - "learning sensor multiplexing design through back propagation"
    - "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-8 (induced by source paper "reconfiguring the imaging pipeline for computer vision"):
  with members:
    - "eie efficient inference engine on compressed deep neural network"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning face representation from scratch"

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)