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: "local binary convolutional neural networks"
  Abstract: We propose local binary convolution (LBC), an efficient alternative to convolutional layers in standard convolutional neural networks (CNN). The design principles of LBC are motivated by local binary patterns (LBP). The LBC layer comprises of a set of fixed sparse pre-defined binary convolutional filters that are not updated during the training process, a non-linear activation function and a set of learnable linear weights. The linear weights combine the activated filter responses to approximate the corresponding activated filter responses of a standard convolutional layer. The LBC layer affords significant parameter savings, 9x to 169x in the number of learnable parameters compared to a standard convolutional layer. Furthermore, the sparse and binary nature of the weights also results in up to 9x to 169x savings in model size compared to a standard convolutional layer. We demonstrate both theoretically and experimentally that our local binary convolution layer is a good approximation of a standard convolutional layer. Empirically, CNNs with LBC layers, called local binary convolutional neural networks (LBCNN), achieves performance parity with regular CNNs on a range of visual datasets (MNIST, SVHN, CIFAR-10, and ImageNet) while enjoying significant computational savings.

Connected through hyperedge HE-1 (induced by source paper "learning architectures for binary networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "binarized neural architecture search"
    - "mobinet a mobile binary network for image classification"
    - "bayesian optimized 1 bit cnns"
    - "searching for a robust neural architecture in four gpu hours"
    - "exploring randomly wired neural networks for image recognition"
    - "xnor net improved binary neural networks"

Connected through hyperedge HE-2 (induced by source paper "searching central difference convolutional networks for face anti spoofing"):
  with members:
    - "video action recognition via neural architecture searching"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "low shot learning from imaginary 3d model"
    - "self paced deep learning for weakly supervised object detection"
    - "object centric auto encoders and dummy anomalies for abnormal event detection in video"
    - "experience replay for continual learning"
    - "hierarchy of gans for learning embodied self awareness model"
    - "a multi perspective approach to anomaly detection for self aware embodied agents"
    - "foil it find one mismatch between image and language caption"

Connected through hyperedge HE-4 (induced by source paper "multi criterion evolutionary design of deep convolutional neural networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "evolutionary neural automl for deep learning"
    - "searching for a robust neural architecture in four gpu hours"
    - "exploring randomly wired neural networks for image recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-5 (induced by source paper "lp 3dcnn unveiling local phase in 3d convolutional neural networks"):
  with members:
    - "modelling local phase of images and textures with applications in phase denoising and phase retrieval"
    - "spatio temporal channel correlation networks for action classification"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "incremental network quantization towards lossless cnns with low precision weights"

Connected through hyperedge HE-6 (induced by source paper "attention based pruning for shift networks"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "learning to prune filters in convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "quantized guided pruning for efficient hardware implementations of convolutional neural networks"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "constructing fast network through deconstruction of convolution"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "nisp pruning networks using neuron importance score propagation"

Connected through hyperedge HE-7 (induced by source paper "binarized neural architecture search"):
  with members:
    - "nas bench 101 towards reproducible neural architecture search"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "dynamic distribution pruning for efficient network architecture search"
    - "multinomial distribution learning for effective neural architecture search"
    - "searching for accurate binary neural architectures"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"
    - "training wide residual networks for deployment using a single bit for each weight"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-8 (induced by source paper "dupnet towards very tiny quantized cnn with improved accuracy for face detection"):
  with members:
    - "squantizer simultaneous learning for both sparse and low precision neural networks"
    - "detnet a backbone network for object detection"
    - "quantization mimic towards very tiny cnn for object detection"
    - "pact parameterized clipping activation for quantized neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep learning with low precision by half wave gaussian quantization"
    - "towards effective low bitwidth convolutional neural networks"
    - "incremental network quantization towards lossless cnns with low precision weights"

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)