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: "hardware software co exploration of neural architectures"
  Abstract: We propose a novel hardware and software co-exploration framework for efficient neural architecture search (NAS). Different from existing hardware-aware NAS which assumes a fixed hardware design and explores the neural architecture search space only, our framework simultaneously explores both the architecture search space and the hardware design space to identify the best neural architecture and hardware pairs that maximize both test accuracy and hardware efficiency. Such a practice greatly opens up the design freedom and pushes forward the Pareto frontier between hardware efficiency and test accuracy for better design tradeoffs. The framework iteratively performs a two-level (fast and slow) exploration. Without lengthy training, the fast exploration can effectively fine-tune hyperparameters and prune inferior architectures in terms of hardware specifications, which significantly accelerates the NAS process. Then, the slow exploration trains candidates on a validation set and updates a controller using the reinforcement learning to maximize the expected accuracy together with the hardware efficiency. Experiments on ImageNet show that our co-exploration NAS can find the neural architectures and associated hardware design with the same accuracy, 35.24% higher throughput, 54.05% higher energy efficiency and 136x reduced search time, compared with the state-of-the-art 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"
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "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 "co exploration of neural architectures and heterogeneous asic accelerator designs targeting multiple tasks"):
  with members:
    - "nass optimizing secure inference via neural architecture search"
    - "achieving super linear speedup across multi fpga for real time dnn inference"
    - "herald optimizing heterogeneous dnn accelerators for edge devices"
    - "on neural architecture search for resource constrained hardware platforms"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"
    - "mnasnet platform aware neural architecture search for mobile"
    - "scnn an accelerator for compressed sparse convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "device circuit architecture co exploration for computing in memory neural accelerators"):
  with members:
    - "video action recognition via neural architecture searching"
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "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-4 (induced by source paper "achieving super linear speedup across multi fpga for real time dnn inference"):
  with members:
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "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-5 (induced by source paper "on neural architecture search for resource constrained hardware platforms"):
  with members:
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "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"

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.LG(Machine Learning)