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: "autodispnet improving disparity estimation with automl"
  Abstract: Much research work in computer vision is being spent on optimizing existing network architectures to obtain a few more percentage points on benchmarks. Recent AutoML approaches promise to relieve us from this effort. However, they are mainly designed for comparatively small-scale classification tasks. In this work, we show how to use and extend existing AutoML techniques to efficiently optimize large-scale U-Net-like encoder-decoder architectures. In particular, we leverage gradient-based neural architecture search and Bayesian optimization for hyperparameter search. The resulting optimization does not require a large-scale compute cluster. We show results on disparity estimation that clearly outperform the manually optimized baseline and reach state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "optimized generic feature learning for few shot classification across domains"):
  with members:
    - "a baseline for few shot image classification"
    - "meta learning with differentiable convex optimization"
    - "large scale adversarial representation learning"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "fast autoaugment"
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"

Connected through hyperedge HE-2 (induced by source paper "automated design of error resilient and hardware efficient deep neural networks"):
  with members:
    - "understanding and robustifying differentiable architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "methods for interpreting and understanding deep neural networks"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"

Connected through hyperedge HE-3 (induced by source paper "meta learning of neural architectures for few shot learning"):
  with members:
    - "meta learning with warped gradient descent"
    - "searching for a robust neural architecture in four gpu hours"
    - "understanding and robustifying differentiable architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "automated design of error resilient and hardware efficient deep neural networks"
    - "fast context adaptation via meta learning"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "transferring knowledge across learning processes"

Connected through hyperedge HE-4 (induced by source paper "understanding and robustifying differentiable architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "best practices for scientific research on neural architecture search"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"

Connected through hyperedge HE-5 (induced by source paper "best practices for scientific research on neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "autogan neural architecture search for generative adversarial networks"
    - "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"
    - "tabular benchmarks for joint architecture and hyperparameter optimization"
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary 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)