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: "alphax exploring neural architectures with deep neural networks and monte carlo tree search"
  Abstract: We present AlphaX, a fully automated agent that designs complex neural architectures from scratch. AlphaX explores the exponentially grown search space with a distributed Monte Carlo Tree Search (MCTS) and a Meta-Deep Neural Network (DNN). MCTS intrinsically improves the search efficiency by dynamically balancing the exploration and exploitation at fine-grained states, while Meta-DNN predicts the network accuracy to guide the search, and to provide an estimated reward to speed up the rollout. As the search progresses, AlphaX also generates the training data for Meta-DNN. So, the learning of Meta-DNN is end-to-end. In 14 days with only 16 GPUs (1832 samples), AlphaX found an architecture that reaches the state-of-the-art accuracies on both CIFAR-10(97.18%) and ImageNet(75.5% top-1 and 92.2% top-5). This demonstrates up to 10x speedup over the original searching for NASNet that used 500 GPUs in 4 days (20000 samples). On NASBench-101, AlphaX demonstrates 3x and 2.8x speedup over Random Search and Regularized Evolution. Finally, we show the searched architecture improves a variety of vision applications from Neural Style Transfer, to Image Captioning and Object Detection. Our implementation is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "efficient sample based neural architecture search with learnable predictor"):
  with members:
    - "nas bench 102 extending the scope of reproducible neural architecture search"
    - "adaptive stochastic natural gradient method for one shot neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "bayesnas a bayesian approach for neural architecture search"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-2 (induced by source paper "neunets an automated synthesis engine for neural network design"):
  with members:
    - "practical deep learning architecture optimization"
    - "massively parallel hyperparameter tuning"
    - "regularized evolution for image classifier architecture search"
    - "incremental training of deep convolutional neural networks"
    - "efficient image dataset classification difficulty estimation for predicting deep learning accuracy"
    - "darts differentiable architecture search"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "neural network acceptability judgments"

Connected through hyperedge HE-3 (induced by source paper "bananas bayesian optimization with neural architectures for neural architecture search"):
  with members:
    - "probo a framework for using probabilistic programming in bayesian optimization"
    - "multi objective neural architecture search via predictive network performance optimization"
    - "random search and reproducibility for neural architecture search"
    - "probo versatile bayesian optimization using any probabilistic programming language"
    - "sample efficient neural architecture search by learning action space"
    - "d vae a variational autoencoder for directed acyclic graphs"
    - "nas bench 101 towards reproducible neural architecture search"
    - "bayesnas a bayesian approach for neural architecture search"

Connected through hyperedge HE-4 (induced by source paper "evaluating the search phase of neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "on network design spaces for visual recognition"
    - "detnas backbone search for object detection"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "nas bench 101 towards reproducible neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-5 (induced by source paper "improving one shot nas by suppressing the posterior fading"):
  with members:
    - "am lfs automl for loss function search"
    - "online hyper parameter learning for auto augmentation strategy"
    - "mixconv mixed depthwise convolutional kernels"
    - "xnas neural architecture search with expert advice"
    - "single path one shot neural architecture search with uniform sampling"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "understanding neural architecture search techniques"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "sample efficient neural architecture search by learning action space"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "detnas backbone search for object detection"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "nas bench 101 towards reproducible neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "evaluating the search phase of neural architecture search"
    - "nas fpn learning scalable feature pyramid architecture for object detection"

Connected through hyperedge HE-7 (induced by source paper "elf opengo an analysis and open reimplementation of alphazero"):
  with members:
    - "from gameplay to symbolic reasoning learning sat solver heuristics in the style of alpha go zero"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "demystifying alphago zero as alphago gan"
    - "better computer go player with neural network and long term prediction"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "move evaluation in go using deep convolutional neural networks"
    - "teaching deep convolutional neural networks to play go"

Connected through hyperedge HE-8 (induced by source paper "multi objective neural architecture search via predictive network performance optimization"):
  with members:
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "adaptive stochastic natural gradient method for one shot neural architecture search"
    - "sample efficient neural architecture search by learning action space"
    - "evaluating the search phase of neural architecture search"
    - "darts differentiable architecture search"

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)