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: "chamnet towards efficient network design through platform aware model adaptation"
  Abstract: This paper proposes an efficient neural network (NN) architecture design methodology called Chameleon that honors given resource constraints. Instead of developing new building blocks or using computationally-intensive reinforcement learning algorithms, our approach leverages existing efficient network building blocks and focuses on exploiting hardware traits and adapting computation resources to fit target latency and/or energy constraints. We formulate platform-aware NN architecture search in an optimization framework and propose a novel algorithm to search for optimal architectures aided by efficient accuracy and resource (latency and/or energy) predictors. At the core of our algorithm lies an accuracy predictor built atop Gaussian Process with Bayesian optimization for iterative sampling. With a one-time building cost for the predictors, our algorithm produces state-of-the-art model architectures on different platforms under given constraints in just minutes. Our results show that adapting computation resources to building blocks is critical to model performance. Without the addition of any bells and whistles, our models achieve significant accuracy improvements against state-of-the-art hand-crafted and automatically designed architectures. We achieve 73.8% and 75.3% top-1 accuracy on ImageNet at 20ms latency on a mobile CPU and DSP. At reduced latency, our models achieve up to 8.5% (4.8%) and 6.6% (9.3%) absolute top-1 accuracy improvements compared to MobileNetV2 and MnasNet, respectively, on a mobile CPU (DSP), and 2.7% (4.6%) and 5.6% (2.6%) accuracy gains over ResNet-101 and ResNet-152, respectively, on an Nvidia GPU (Intel CPU).

Connected through hyperedge HE-1 (induced by source paper "metapruning meta learning for automatic neural network channel pruning"):
  with members:
    - "meta sr a magnification arbitrary network for super resolution"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "metaanchor learning to detect objects with customized anchors"
    - "slimmable neural networks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "amc automl for model compression and acceleration on mobile devices"
    - "dpp net device aware progressive search for pareto optimal neural architectures"

Connected through hyperedge HE-2 (induced by source paper "densely connected search space for more flexible neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "network pruning via transformable architecture search"
    - "vargnet variable group convolutional neural network for efficient embedded computing"
    - "exploring randomly wired neural networks for image recognition"
    - "customizable architecture search for semantic segmentation"
    - "searching for mobilenetv3"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "mmdetection open mmlab detection toolbox and benchmark"

Connected through hyperedge HE-3 (induced by source paper "vargnet variable group convolutional neural network for efficient embedded computing"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "dnnvm end to end compiler leveraging heterogeneous optimizations on fpga based cnn accelerators"
    - "panoptic feature pyramid networks"
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "tvm end to end optimization stack for deep learning"

Connected through hyperedge HE-4 (induced by source paper "single path one shot neural architecture search with uniform sampling"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "regularized evolution for image classifier architecture search"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "neural epitome search for architecture agnostic network compression"):
  with members:
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "low bit quantization of neural networks for efficient inference"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-6 (induced by source paper "hardware guided symbiotic training for compact accurate yet execution efficient lstm"):
  with members:
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "deepheart semi supervised sequence learning for cardiovascular risk prediction"
    - "an analysis of neural language modeling at multiple scales"
    - "grow and prune compact fast and accurate lstms"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "block sparse recurrent neural networks"
    - "mnasnet platform aware neural architecture search for mobile"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-7 (induced by source paper "tuning algorithms and generators for efficient edge inference"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "fully learnable group convolution for acceleration of deep neural networks"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "learning grouped convolution for efficient domain adaptation"
    - "mpdcompress matrix permutation decomposition algorithm for deep neural network compression"
    - "block sparse recurrent neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "single path nas device aware efficient convnet design"):
  with members:
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "single path one shot neural architecture search with uniform sampling"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"

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)