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: "learnable embedding space for efficient neural architecture compression"
  Abstract: We propose a method to incrementally learn an embedding space over the domain of network architectures, to enable the careful selection of architectures for evaluation during compressed architecture search. Given a teacher network, we search for a compressed network architecture by using Bayesian Optimization (BO) with a kernel function defined over our proposed embedding space to select architectures for evaluation. We demonstrate that our search algorithm can significantly outperform various baseline methods, such as random search and reinforcement learning (Ashok et al., 2018). The compressed architectures found by our method are also better than the state-of-the-art manually-designed compact architecture ShuffleNet (Zhang et al., 2018). We also demonstrate that the learned embedding space can be transferred to new settings for architecture search, such as a larger teacher network or a teacher network in a different architecture family, without any training. Code is publicly available here: this https URL .

Connected through hyperedge HE-1 (induced by source paper "nat neural architecture transformer for accurate and compact architectures"):
  with members:
    - "multi marginal wasserstein gan"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "the evolved transformer"
    - "graph convolutional networks for temporal action localization"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "adversarial learning with local coordinate coding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "structured pruning of neural networks with budget aware regularization"

Connected through hyperedge HE-2 (induced by source paper "budgeted training rethinking deep neural network training under resource constraints"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "deep rigid instance scene flow"
    - "gradient descent finds global minima of deep neural networks"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "darts differentiable architecture search"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-3 (induced by source paper "zero shot task adaptation by homoiconic meta mapping"):
  with members:
    - "lgm net learning to generate matching networks for few shot learning"
    - "online meta learning"
    - "zero shot task transfer"
    - "task2vec task embedding for meta learning"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "meta learning with latent embedding optimization"
    - "universal successor features approximators"
    - "graph hypernetworks for neural architecture search"

Connected through hyperedge HE-4 (induced by source paper "one shot neural architecture search via compressive sensing"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "exploring randomly wired neural networks for image recognition"
    - "evaluating the search phase of neural architecture search"
    - "massively parallel hyperparameter tuning"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "auto keras an efficient neural architecture search system"

Connected through hyperedge HE-5 (induced by source paper "random search and reproducibility for neural architecture search"):
  with members:
    - "massively parallel hyperparameter tuning"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "snas stochastic neural 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)