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: "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
  Abstract: A fundamental question lies in almost every application of deep neural networks: what is the optimal neural architecture given a specific dataset? Recently, several Neural Architecture Search (NAS) frameworks have been developed that use reinforcement learning and evolutionary algorithm to search for the solution. However, most of them take a long time to find the optimal architecture due to the huge search space and the lengthy training process needed to evaluate each candidate. In addition, most of them aim at accuracy only and do not take into consideration the hardware that will be used to implement the architecture. This will potentially lead to excessive latencies beyond specifications, rendering the resulting architectures useless. To address both issues, in this paper we use Field Programmable Gate Arrays (FPGAs) as a vehicle to present a novel hardware-aware NAS framework, namely FNAS, which will provide an optimal neural architecture with latency guaranteed to meet the specification. In addition, with a performance abstraction model to analyze the latency of neural architectures without training, our framework can quickly prune architectures that do not satisfy the specification, leading to higher efficiency. Experimental results on common data set such as ImageNet show that in the cases where the state-of-the-art generates architectures with latencies 7.81x longer than the specification, those from FNAS can meet the specs with less than 1% accuracy loss. Moreover, FNAS also achieves up to 11.13x speedup for the search process. To the best of the authors' knowledge, this is the very first hardware aware NAS.

Connected through hyperedge HE-1 (induced by source paper "nass optimizing secure inference via neural architecture search"):
  with members:
    - "video action recognition via neural architecture searching"
    - "hardware software co exploration of neural architectures"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "on neural architecture search for resource constrained hardware platforms"
    - "device circuit architecture co exploration for computing in memory neural accelerators"
    - "xonn xnor based oblivious deep neural network inference"
    - "prada protecting against dnn model stealing attacks"
    - "gazelle a low latency framework for secure neural network inference"

Connected through hyperedge HE-2 (induced by source paper "low overhead online data flow tracking for intermittently powered non volatile fpgas"):
  with members:
    - "achieving super linear speedup across multi fpga for real time dnn inference"

Connected through hyperedge HE-3 (induced by source paper "skynet a hardware efficient method for object detection and tracking on embedded systems"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "skynet a champion model for dac sdc on low power object detection"
    - "single path nas device aware efficient convnet design"
    - "ternary hybrid neural tree networks for highly constrained iot applications"
    - "searching for mobilenetv3"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "resource constrained neural network architecture search"

Connected through hyperedge HE-4 (induced by source paper "device circuit architecture co exploration for computing in memory neural accelerators"):
  with members:
    - "video action recognition via neural architecture searching"
    - "hardware software co exploration of neural architectures"
    - "achieving super linear speedup across multi fpga for real time dnn inference"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "on neural architecture search for resource constrained hardware platforms"
    - "architecture search of dynamic cells for semantic video segmentation"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-5 (induced by source paper "achieving super linear speedup across multi fpga for real time dnn inference"):
  with members:
    - "hardware software co exploration of neural architectures"
    - "on the universal approximability and complexity bounds of quantized relu neural networks"
    - "quantization of fully convolutional networks for accurate biomedical image segmentation"
    - "an unsupervised learning model for deformable medical image registration"
    - "in datacenter performance analysis of a tensor processing unit"
    - "recent trends in deep learning based natural language processing"
    - "maximizing cnn accelerator efficiency through resource partitioning"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-6 (induced by source paper "machine vision guided 3d medical image compression for efficient transmission and accurate segmentation in the clouds"):
  with members:
    - "scnn a general distribution based statistical convolutional neural network with application to video object detection"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "dilated convolutional neural networks for cardiovascular mr segmentation in congenital heart disease"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "3d deeply supervised network for automatic liver segmentation from ct volumes"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "on neural architecture search for resource constrained hardware platforms"):
  with members:
    - "hardware software co exploration of neural architectures"
    - "exploiting computation power of blockchain for biomedical image segmentation"
    - "scnn a general distribution based statistical convolutional neural network with application to video object detection"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"
    - "mnasnet platform aware neural architecture search for mobile"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-8 (induced by source paper "when single event upset meets deep neural networks observations explorations and remedies"):
  with members:
    - "achieving super linear speedup across multi fpga for real time dnn inference"
    - "learning phase competition for traffic signal control"
    - "intrinsic image transformation via scale space decomposition"
    - "neural color transfer between images"
    - "on the robustness of convolutional neural networks to internal architecture and weight perturbations"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "network in network"

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.DC(Distributed, Parallel, and Cluster Computing)