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: "embedded binarized neural networks"
  Abstract: We study embedded Binarized Neural Networks (eBNNs) with the aim of allowing current binarized neural networks (BNNs) in the literature to perform feedforward inference efficiently on small embedded devices. We focus on minimizing the required memory footprint, given that these devices often have memory as small as tens of kilobytes (KB). Beyond minimizing the memory required to store weights, as in a BNN, we show that it is essential to minimize the memory used for temporaries which hold intermediate results between layers in feedforward inference. To accomplish this, eBNN reorders the computation of inference while preserving the original BNN structure, and uses just a single floating-point temporary for the entire neural network. All intermediate results from a layer are stored as binary values, as opposed to floating-points used in current BNN implementations, leading to a 32x reduction in required temporary space. We provide empirical evidence that our proposed eBNN approach allows efficient inference (10s of ms) on devices with severely limited memory (10s of KB). For example, eBNN achieves 95\% accuracy on the MNIST dataset running on an Intel Curie with only 15 KB of usable memory with an inference runtime of under 50 ms per sample. To ease the development of applications in embedded contexts, we make our source code available that allows users to train and discover eBNN models for a learning task at hand, which fit within the memory constraint of the target device.

Connected through hyperedge HE-1 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "realtime scheduling and power allocation using deep neural networks"
    - "over the air deep learning based radio signal classification"
    - "active learning for wireless iot intrusion detection"
    - "electrosense open and big spectrum data"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "adversarial attacks on deep learning based radio signal classification"

Connected through hyperedge HE-2 (induced by source paper "making logic learnable with neural networks"):
  with members:
    - "lutnet learning fpga configurations for highly efficient neural network inference"
    - "circuit based intrinsic methods to detect overfitting"
    - "learning a sat solver from single bit supervision"
    - "techniques for interpretable machine learning"
    - "fast obdd reordering using neural message passing on hypergraph"
    - "graph neural networks and boolean satisfiability"
    - "finn a framework for fast scalable binarized neural network inference"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "quantitative verification of neural networks and its security applications"):
  with members:
    - "phase transition behavior of cardinality and xor constraints"
    - "adversarial examples are a natural consequence of test error in noise"
    - "strip a defence against trojan attacks on deep neural networks"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "adversarial spheres"

Connected through hyperedge HE-4 (induced by source paper "a unified optimization approach for cnn model inference on integrated gpus"):
  with members:
    - "neurostream scalable and energy efficient deep learning with smart memory cubes"
    - "inference of quantized neural networks on heterogeneous all programmable devices"
    - "yolov3 an incremental improvement"
    - "learning to optimize tensor programs"
    - "optimizing cnn model inference on cpus"
    - "mnasnet platform aware neural architecture search for mobile"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "musical chair efficient real time recognition using collaborative iot devices"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "privacy preserving human activity recognition from extreme low resolution"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "ternary weight networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "event triggered natural hazard monitoring with convolutional neural networks on the edge"):
  with members:
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "low memory convolutional neural networks through incremental depth first processing"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "a comparison on audio signal preprocessing methods for deep neural networks on music tagging"
    - "hello edge keyword spotting on microcontrollers"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-7 (induced by source paper "incomplete dot products for dynamic computation scaling in neural network inference"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "learning structured sparsity in deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "federated learning of deep networks using model averaging"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-8 (induced by source paper "distributed deep neural networks over the cloud the edge and end devices"):
  with members:
    - "branchynet fast inference via early exiting from deep neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "spatially adaptive computation time for residual networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "multimodal sparse representation learning and applications"
    - "deep residual learning for image recognition"
    - "firecaffe near linear acceleration of deep neural network training on compute clusters"
    - "adam a method for stochastic optimization"

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)