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: "understanding neural architecture search techniques"
  Abstract: Automatic methods for generating state-of-the-art neural network architectures without human experts have generated significant attention recently. This is because of the potential to remove human experts from the design loop which can reduce costs and decrease time to model deployment. Neural architecture search (NAS) techniques have improved significantly in their computational efficiency since the original NAS was proposed. This reduction in computation is enabled via weight sharing such as in Efficient Neural Architecture Search (ENAS). However, recently a body of work confirms our discovery that ENAS does not do significantly better than random search with weight sharing, contradicting the initial claims of the authors. #R##N#We provide an explanation for this phenomenon by investigating the interpretability of the ENAS controller's hidden state. We are interested in seeing if the controller embeddings are predictive of any properties of the final architecture - for example, graph properties like the number of connections, or validation performance. We find models sampled from identical controller hidden states have no correlation in various graph similarity metrics. This failure mode implies the RNN controller does not condition on past architecture choices. Importantly, we may need to condition on past choices if certain connection patterns prevent vanishing or exploding gradients. #R##N#Lastly, we propose a solution to this failure mode by forcing the controller's hidden state to encode pasts decisions by training it with a memory buffer of previously sampled architectures. Doing this improves hidden state interpretability by increasing the correlation controller hidden states and graph similarity metrics.

Connected through hyperedge HE-1 (induced by source paper "fairnas rethinking evaluation fairness of weight sharing neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "understanding and robustifying differentiable architecture search"
    - "mixconv mixed depthwise convolutional kernels"
    - "multi objective reinforced evolution in mobile neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "evaluating the search phase of neural architecture search"

Connected through hyperedge HE-2 (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"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "alphax exploring neural architectures with deep neural networks and monte carlo tree search"

Connected through hyperedge HE-3 (induced by source paper "a study of the learning progress in neural architecture search techniques"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "neural architecture optimization"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "smash one shot model architecture search through hypernetworks"
    - "neural architecture search with reinforcement learning"

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)