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: "conditional deep learning for energy efficient and enhanced pattern recognition"
  Abstract: Deep learning neural networks have emerged as one of the most powerful classification tools for vision related applications. However, the computational and energy requirements associated with such deep nets can be quite high, and hence their energy-efficient implementation is of great interest. Although traditionally the entire network is utilized for the recognition of all inputs, we observe that the classification difficulty varies widely across inputs in real-world datasets; only a small fraction of inputs require the full computational effort of a network, while a large majority can be classified correctly with very low effort. In this paper, we propose Conditional Deep Learning (CDL) where the convolutional layer features are used to identify the variability in the difficulty of input instances and conditionally activate the deeper layers of the network. We achieve this by cascading a linear network of output neurons for each convolutional layer and monitoring the output of the linear network to decide whether classification can be terminated at the current stage or not. The proposed methodology thus enables the network to dynamically adjust the computational effort depending upon the difficulty of the input data while maintaining competitive classification accuracy. We evaluate our approach on the MNIST dataset. Our experiments demonstrate that our proposed CDL yields 1.91x reduction in average number of operations per input, which translates to 1.84x improvement in energy. In addition, our results show an improvement in classification accuracy from 97.5% to 98.9% as compared to the original network.

Connected through hyperedge HE-1 (induced by source paper "relevant features based auxiliary cells for energy efficient detection of natural errors"):
  with members:
    - "natural and adversarial error detection using invariance to image transformations"
    - "designing adaptive neural networks for energy constrained image classification"
    - "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "distance based confidence score for neural network classifiers"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"

Connected through hyperedge HE-2 (induced by source paper "dynamic convolutions exploiting spatial sparsity for faster inference"):
  with members:
    - "batch shaping for learning conditional channel gated networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sbnet sparse blocks network for fast inference"
    - "mnasnet platform aware neural architecture search for mobile"
    - "dynamic channel pruning feature boosting and suppression"
    - "skipnet learning dynamic routing in convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "an analysis of deep neural network models for practical applications"

Connected through hyperedge HE-3 (induced by source paper "s2dnas transforming static cnn model for dynamic inference via neural architecture search"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "sbnet sparse blocks network for fast inference"
    - "darts differentiable architecture search"
    - "mnasnet platform aware neural architecture search for mobile"
    - "dynamic channel pruning feature boosting and suppression"
    - "skipnet learning dynamic routing in convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "approxnet content and contention aware video analytics system for the edge"):
  with members:
    - "focus querying large video datasets with low latency and low cost"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "blockdrop dynamic inference paths in residual networks"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "multi scale dense networks for resource efficient image classification"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-5 (induced by source paper "one size does not fit all quantifying and exposing the accuracy latency trade off in machine learning cloud service apis via tolerance tiers"):
  with members:
    - "branchynet fast inference via early exiting from deep neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "clipper a low latency online prediction serving system"
    - "the microsoft 2016 conversational speech recognition system"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "robust and computationally efficient anomaly detection using powers of two networks"):
  with members:
    - "efficient gan based anomaly detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "a survey of model compression and acceleration for deep neural networks"
    - "context encoders feature learning by inpainting"
    - "plug and play cnn for crowd motion analysis an application in abnormal event detection"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "online sparse system identification and signal reconstruction using projections onto weighted ell_ 1 balls"

Connected through hyperedge HE-7 (induced by source paper "secs efficient deep stream processing via class skew dichotomy"):
  with members:
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "eva 2 exploiting temporal redundancy in live computer vision"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "lcnn lookup based convolutional neural network"
    - "densely connected convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "designing adaptive neural networks for energy constrained image classification"):
  with members:
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
    - "adaptive neural networks for efficient inference"
    - "neuralpower predict and deploy energy efficient convolutional neural networks"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "hyperpower power and memory constrained hyper parameter optimization for neural networks"
    - "nest a neural network synthesis tool based on a grow and prune paradigm"

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)