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: "discovering neural wirings"
  Abstract: The success of neural networks has driven a shift in focus from feature engineering to architecture engineering. However, successful networks today are constructed using a small and manually defined set of building blocks. Even in methods of neural architecture search (NAS) the network connectivity patterns are largely constrained. In this work we propose a method for discovering neural wirings. We relax the typical notion of layers and instead enable channels to form connections independent of each other. This allows for a much larger space of possible networks. The wiring of our network is not fixed during training -- as we learn the network parameters we also learn the structure itself. Our experiments demonstrate that our learned connectivity outperforms hand engineered and randomly wired networks. By learning the connectivity of MobileNetV1we boost the ImageNet accuracy by 10% at ~41M FLOPs. Moreover, we show that our method generalizes to recurrent and continuous time networks. Our work may also be regarded as unifying core aspects of the neural architecture search problem with sparse neural network learning. As NAS becomes more fine grained, finding a good architecture is akin to finding a sparse subnetwork of the complete graph. Accordingly, DNW provides an effective mechanism for discovering sparse subnetworks of predefined architectures in a single training run. Though we only ever use a small percentage of the weights during the forward pass, we still play the so-called initialization lottery with a combinatorial number of subnetworks. Code and pretrained models are available at this https URL while additional visualizations may be found at this https URL.

Connected through hyperedge HE-1 (induced by source paper "ordering chaos memory aware scheduling of irregularly wired neural networks for edge devices"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "automatic model selection for neural networks"
    - "swiftnet using graph propagation as meta knowledge to search highly representative neural architectures"
    - "pytorch an imperative style high performance deep learning library"
    - "learned step size quantization"
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "amc automl for model compression and acceleration on mobile devices"
    - "glow graph lowering compiler techniques for neural networks"

Connected through hyperedge HE-2 (induced by source paper "soft threshold weight reparameterization for learnable sparsity"):
  with members:
    - "rigging the lottery making all tickets winners"
    - "sparse networks from scratch faster training without losing performance"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "fast sparse convnets"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "butterfly transform an efficient fft based neural architecture design"
    - "green ai"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-3 (induced by source paper "chameleon adaptive code optimization for expedited deep neural network compilation"):
  with members:
    - "ordering chaos memory aware scheduling of irregularly wired neural networks for edge devices"
    - "reinforced genetic algorithm learning for optimizing computation graphs"
    - "exploring randomly wired neural networks for image recognition"
    - "active learning for regression using greedy sampling"
    - "meta gradient reinforcement learning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "learning scheduling algorithms for data processing clusters"
    - "learning to optimize tensor programs"

Connected through hyperedge HE-4 (induced by source paper "dicenet dimension wise convolutions for efficient networks"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "searching for mobilenetv3"
    - "mixconv mixed depthwise convolutional kernels"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"

Connected through hyperedge HE-5 (induced by source paper "class dependent compression of deep neural networks"):
  with members:
    - "knowledge squeezed adversarial network compression"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "exploring randomly wired neural networks for image recognition"
    - "class balanced loss based on effective number of samples"
    - "darc differentiable architecture compression"
    - "compressing gans using knowledge distillation"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"

Connected through hyperedge HE-6 (induced by source paper "searching for stage wise neural graphs in the limit"):
  with members:
    - "on network design spaces for visual recognition"
    - "detnas backbone search for object detection"
    - "assemblenet searching for multi stream neural connectivity in video architectures"
    - "exploring randomly wired neural networks for image recognition"
    - "nas bench 101 towards reproducible neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "do imagenet classifiers generalize to imagenet"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-7 (induced by source paper "what s hidden in a randomly weighted neural network"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "exploring randomly wired neural networks for image recognition"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "mnasnet platform aware neural architecture search for mobile"
    - "progressive neural architecture search"

Connected through hyperedge HE-8 (induced by source paper "autoshrink a topology aware nas for discovering efficient neural architecture"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "mnasnet platform aware neural architecture search for mobile"
    - "on the state of the art of evaluation in neural language models"

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)