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: "fixynn efficient hardware for mobile computer vision via transfer learning"
  Abstract: The computational demands of computer vision tasks based on state-of-the-art Convolutional Neural Network (CNN) image classification far exceed the energy budgets of mobile devices. This paper proposes FixyNN, which consists of a fixed-weight feature extractor that generates ubiquitous CNN features, and a conventional programmable CNN accelerator which processes a dataset-specific CNN. Image classification models for FixyNN are trained end-to-end via transfer learning, with the common feature extractor representing the transfered part, and the programmable part being learnt on the target dataset. Experimental results demonstrate FixyNN hardware can achieve very high energy efficiencies up to 26.6 TOPS/W ($4.81 \times$ better than iso-area programmable accelerator). Over a suite of six datasets we trained models via transfer learning with an accuracy loss of $<1\%$ resulting in up to 11.2 TOPS/W - nearly $2 \times$ more efficient than a conventional programmable CNN accelerator of the same area.

Connected through hyperedge HE-1 (induced by source paper "searching for winograd aware quantized networks"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "winograd convolution for dnns beyond linear polynomials"
    - "visual wake words dataset"
    - "sparse sparse architecture search for cnns on resource constrained microcontrollers"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "searching for mobilenetv3"
    - "efficient winograd convolution via integer arithmetic"
    - "differentiable soft quantization bridging full precision and low bit neural networks"

Connected through hyperedge HE-2 (induced by source paper "leep a new measure to evaluate transferability of learned representations"):
  with members:
    - "dataset2vec learning dataset meta features"
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "a baseline for few shot image classification"
    - "which tasks should be learned together in multi task learning"
    - "p2l predicting transfer learning for images and semantic relations"
    - "transferability and hardness of supervised classification tasks"
    - "task2vec task embedding for meta learning"
    - "meta dataset a dataset of datasets for learning to learn from few examples"

Connected through hyperedge HE-3 (induced by source paper "flexibo cost aware multi objective optimization of deep neural networks"):
  with members:
    - "transfer learning for performance modeling of deep neural network systems"
    - "learning software configuration spaces a systematic literature review"
    - "multi objective bayesian optimisation with preferences over objectives"
    - "a flexible framework for multi objective bayesian optimization using random scalarizations"
    - "ordered preference elicitation strategies for supporting multi objective decision making"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "practical design space exploration"

Connected through hyperedge HE-4 (induced by source paper "noisy machines understanding noisy neural networks and enhancing robustness to analog hardware errors using distillation"):
  with members:
    - "zero shot knowledge transfer via adversarial belief matching"
    - "adversarially robust distillation"
    - "accurate deep neural network inference using computational phase change memory"
    - "neuromorphic computing with multi memristive synapses"
    - "mixed precision in memory computing"
    - "all optical machine learning using diffractive deep neural networks"
    - "model compression via distillation and quantization"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"

Connected through hyperedge HE-5 (induced by source paper "tag less back translation"):
  with members:
    - "tagged back translation"
    - "using monolingual data in neural machine translation a systematic study"
    - "generalizing back translation in neural machine translation"
    - "adaptation of machine translation models with back translated data using transductive data selection methods"
    - "pay less attention with lightweight and dynamic convolutions"
    - "investigating backtranslation in neural machine translation"
    - "back translation sampling by targeting difficult words in neural machine translation"
    - "trivial transfer learning for low resource neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "asv accelerated stereo vision system"):
  with members:
    - "packing sparse convolutional neural networks for efficient systolic array implementations column combining under joint optimization"
    - "dnn dataflow choice is overrated"
    - "morph flexible acceleration for 3d cnn based video understanding"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "scale sim systolic cnn accelerator simulator"
    - "mobile machine learning hardware at arm a systems on chip soc perspective"
    - "eva 2 exploiting temporal redundancy in live computer vision"

Connected through hyperedge HE-7 (induced by source paper "training dnn iot applications for deployment on analog nvm crossbars"):
  with members:
    - "sparse sparse architecture search for cnns on resource constrained microcontrollers"
    - "differentiable quantization of deep neural networks"
    - "accurate deep neural network inference using computational phase change memory"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "haq hardware aware automated quantization with mixed precision"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"

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)