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: "adaptive stochastic natural gradient method for one shot neural architecture search"
  Abstract: High sensitivity of neural architecture search (NAS) methods against their input such as step-size (i.e., learning rate) and search space prevents practitioners from applying them out-of-the-box to their own problems, albeit its purpose is to automate a part of tuning process. Aiming at a fast, robust, and widely-applicable NAS, we develop a generic optimization framework for NAS. We turn a coupled optimization of connection weights and neural architecture into a differentiable optimization by means of stochastic relaxation. It accepts arbitrary search space (widely-applicable) and enables to employ a gradient-based simultaneous optimization of weights and architecture (fast). We propose a stochastic natural gradient method with an adaptive step-size mechanism built upon our theoretical investigation (robust). Despite its simplicity and no problem-dependent parameter tuning, our method exhibited near state-of-the-art performances with low computational budgets both on image classification and inpainting tasks.

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"
    - "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"
    - "alphax exploring neural architectures with deep neural networks and monte carlo tree search"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-2 (induced by source paper "efficient neural architecture search via proximal iterations"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "single path one shot neural architecture search with uniform sampling"
    - "bayesnas a bayesian approach for neural architecture search"
    - "amc automl for model compression and acceleration on mobile devices"
    - "regularized evolution for image classifier architecture search"
    - "proxquant quantized neural networks via proximal operators"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-3 (induced by source paper "blockwisely supervised neural architecture search with knowledge distillation"):
  with members:
    - "nas evaluation is frustratingly hard"
    - "scarlet nas bridging the gap between stability and scalability in weight sharing neural architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "mixconv mixed depthwise convolutional kernels"
    - "improving one shot nas by suppressing the posterior fading"
    - "single path one shot neural architecture search with uniform sampling"
    - "moga searching beyond mobilenetv3"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"

Connected through hyperedge HE-4 (induced by source paper "searching to exploit memorization effect in learning from corrupted labels"):
  with members:
    - "efficient neural architecture search via proximal iterations"
    - "how does disagreement help generalization against label corruption"
    - "searching for interaction functions in collaborative filtering"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "joint optimization framework for learning with noisy labels"
    - "taking human out of learning applications a survey on automated machine learning"
    - "dimensionality driven learning with noisy labels"
    - "co teaching robust training of deep neural networks with extremely noisy labels"

Connected through hyperedge HE-5 (induced by source paper "understanding architectures learnt by cell based neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "exploring randomly wired neural networks for image recognition"
    - "xnas neural architecture search with expert advice"
    - "evaluating the search phase of neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "darts differentiable architecture search"

Connected through hyperedge HE-6 (induced by source paper "controlling model complexity in probabilistic model based dynamic optimization of neural network structures"):
  with members:
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "dynamic optimization of neural network structures using probabilistic modeling"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "efficient neural architecture search via parameter sharing"
    - "mnasnet platform aware neural architecture search for mobile"
    - "learning efficient convolutional networks through network slimming"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-7 (induced by source paper "neural recurrent structure search for knowledge graph embedding"):
  with members:
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "convolutional 2d knowledge graph embeddings"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "designing neural network architectures using reinforcement learning"
    - "modeling relation paths for representation learning of knowledge bases"

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"
    - "sample efficient neural architecture search by learning action space"
    - "evaluating the search phase of neural architecture search"
    - "alphax exploring neural architectures with deep neural networks and monte carlo tree 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.LG(Machine Learning)