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: "sigma delta quantized networks"
  Abstract: Deep neural networks can be obscenely wasteful. When processing video, a convolutional network expends a fixed amount of computation for each frame with no regard to the similarity between neighbouring frames. As a result, it ends up repeatedly doing very similar computations. To put an end to such waste, we introduce Sigma-Delta networks. With each new input, each layer in this network sends a discretized form of its change in activation to the next layer. Thus the amount of computation that the network does scales with the amount of change in the input and layer activations, rather than the size of the network. We introduce an optimization method for converting any pre-trained deep network into an optimally efficient Sigma-Delta network, and show that our algorithm, if run on the appropriate hardware, could cut at least an order of magnitude from the computational cost of processing video data.

Connected through hyperedge HE-1 (induced by source paper "ebpc extended bit plane compression for deep neural network inference and training accelerators"):
  with members:
    - "and the bit goes down revisiting the quantization of neural networks"
    - "additive noise annealing and approximation properties of quantized neural networks"
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "rpr random partition relaxation for training binary and ternary weight neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "multiposenet fast multi person pose estimation using pose residual network"

Connected through hyperedge HE-2 (induced by source paper "a survey on methods and theories of quantized neural networks"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "training and inference with integers in deep neural networks"
    - "model compression via distillation and quantization"
    - "loss aware weight quantization of deep networks"
    - "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
    - "minimum energy quantized neural networks"
    - "network sketching exploiting binary structure in deep cnns"
    - "wrpn wide reduced precision networks"

Connected through hyperedge HE-3 (induced by source paper "cbinfer exploiting frame to frame locality for faster convolutional network inference on video streams"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "a comparison of cnn based face and head detectors for real time video surveillance applications"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-4 (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-5 (induced by source paper "dnn feature map compression using learned representation over gf 2"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "submanifold sparse convolutional networks"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "hardware oriented approximation of convolutional neural networks"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-6 (induced by source paper "fast retinomorphic event stream for video recognition and reinforcement learning"):
  with members:
    - "video enhancement with task oriented flow"
    - "end to end learning of motion representation for video understanding"
    - "efficient parallel methods for deep reinforcement learning"
    - "real time panoramic tracking for event cameras"
    - "hidden two stream convolutional networks for action recognition"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "delving deeper into convolutional networks for learning video representations"
    - "network in network"

Connected through hyperedge HE-7 (induced by source paper "temporally efficient deep learning with spikes"):
  with members:
    - "deep spiking networks"
    - "deep counter networks for asynchronous event based processing"
    - "fast and efficient asynchronous neural computation with adapting spiking neural networks"
    - "training deep spiking neural networks using backpropagation"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "an objective function for stdp"

Connected through hyperedge HE-8 (induced by source paper "delta networks for optimized recurrent network computation"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "learning a driving simulator"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "deep networks with stochastic depth"
    - "end to end learning for self driving cars"
    - "a curriculum learning method for improved noise robustness in automatic speech recognition"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "recurrent neural networks with limited numerical precision"

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.NE(Neural and Evolutionary Computing)