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: "exploring randomly wired neural networks for image recognition"
  Abstract: Neural networks for image recognition have evolved through extensive manual design from simple chain-like models to structures with multiple wiring paths. The success of ResNets and DenseNets is due in large part to their innovative wiring plans. Now, neural architecture search (NAS) studies are exploring the joint optimization of wiring and operation types, however, the space of possible wirings is constrained and still driven by manual design despite being searched. In this paper, we explore a more diverse set of connectivity patterns through the lens of randomly wired neural networks. To do this, we first define the concept of a stochastic network generator that encapsulates the entire network generation process. Encapsulation provides a unified view of NAS and randomly wired networks. Then, we use three classical random graph models to generate randomly wired graphs for networks. The results are surprising: several variants of these random generators yield network instances that have competitive accuracy on the ImageNet benchmark. These results suggest that new efforts focusing on designing better network generators may lead to new breakthroughs by exploring less constrained search spaces with more room for novel design.

Connected through hyperedge HE-1 (induced by source paper "automl zero evolving machine learning algorithms from scratch"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "towards modular and programmable architecture search"
    - "weight agnostic neural networks"
    - "the evolved transformer"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "atomnas fine grained end to end neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "learning architectures for binary networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "binarized neural architecture search"
    - "mobinet a mobile binary network for image classification"
    - "bayesian optimized 1 bit cnns"
    - "searching for a robust neural architecture in four gpu hours"
    - "xnor net improved binary neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "nas count counting by density with neural architecture search"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "detnas backbone search for object detection"
    - "the evolved transformer"
    - "nas bench 101 towards reproducible neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "darts improved differentiable architecture search with early stopping"

Connected through hyperedge HE-4 (induced by source paper "ordering chaos memory aware scheduling of irregularly wired neural networks for edge devices"):
  with members:
    - "automatic model selection for neural networks"
    - "swiftnet using graph propagation as meta knowledge to search highly representative neural architectures"
    - "discovering neural wirings"
    - "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-5 (induced by source paper "search for better students to learn distilled knowledge"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "darts differentiable architecture search"
    - "understanding individual decisions of cnns via contrastive backpropagation"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-6 (induced by source paper "recent advances in scalable network generation"):
  with members:
    - "efficiently generating geometric inhomogeneous and hyperbolic random graphs"
    - "parallel weighted random sampling"
    - "linear work generation of r mat graphs"
    - "egbter capturing degree distribution clustering coefficients and community structure in a single random graph model"
    - "multiscale planar graph generation"
    - "parallel and i o efficient randomisation of massive networks using global curveball trades"
    - "efficient parallel random sampling vectorized cache efficient and online"
    - "communication free massively distributed graph generation"

Connected through hyperedge HE-7 (induced by source paper "an adaptive random path selection approach for incremental learning"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "continual lifelong learning with neural networks a review"
    - "random path selection for incremental learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "deep generative dual memory network for continual learning"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-8 (induced by source paper "neural inheritance relation guided one shot layer assignment search"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "autogrow automatic layer growing in deep convolutional networks"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "autoslim towards one shot architecture search for channel numbers"
    - "mfas multimodal fusion architecture search"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "efficientnet rethinking model scaling for convolutional neural networks"

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)