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: "towards closing the energy gap between hog and cnn features for embedded vision"
  Abstract: Computer vision enables a wide range of applications in robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics. For many of these applications, local embedded processing is preferred due to privacy and/or latency concerns. Accordingly, energy-efficient embedded vision hardware delivering real-time and robust performance is crucial. While deep learning is gaining popularity in several computer vision algorithms, a significant energy consumption difference exists compared to traditional hand-crafted approaches. In this paper, we provide an in-depth analysis of the computation, energy and accuracy trade-offs between learned features such as deep Convolutional Neural Networks (CNN) and hand-crafted features such as Histogram of Oriented Gradients (HOG). This analysis is supported by measurements from two chips that implement these algorithms. Our goal is to understand the source of the energy discrepancy between the two approaches and to provide insight about the potential areas where CNNs can be improved and eventually approach the energy-efficiency of HOG while maintaining its outstanding performance accuracy.

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"
    - "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"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"

Connected through hyperedge HE-2 (induced by source paper "outstanding bit error tolerance of resistive ram based binarized neural networks"):
  with members:
    - "in memory and error immune differential rram implementation of binarized deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "ecc platform independent energy constrained deep neural network compression via a bilinear regression model"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "amc automl for model compression and acceleration on mobile devices"
    - "energy constrained compression for deep neural networks via weighted sparse projection and layer input masking"
    - "a unified framework of dnn weight pruning and weight clustering quantization using admm"
    - "compressing neural networks using the variational information bottleneck"
    - "learning efficient convolutional networks through network slimming"
    - "self normalizing neural networks"

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"
    - "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"
    - "exploring computation communication tradeoffs in camera systems"

Connected through hyperedge HE-5 (induced by source paper "sips succinct interest points from unsupervised inlierness probability learning"):
  with members:
    - "lf net learning local features from images"
    - "neighbourhood consensus networks"
    - "interest point detectors stability evaluation on apolloscape dataset"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "superpoint self supervised interest point detection and description"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "learning to find good correspondences"

Connected through hyperedge HE-6 (induced by source paper "energy efficient hardware for on device cnn inference via transfer learning"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "how transferable are features in deep neural networks"

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)