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: "making a science of model search"
  Abstract: Many computer vision algorithms depend on a variety of parameter choices and settings that are typically hand-tuned in the course of evaluating the algorithm. While such parameter tuning is often presented as being incidental to the algorithm, correctly setting these parameter choices is frequently critical to evaluating a method's full potential. Compounding matters, these parameters often must be re-tuned when the algorithm is applied to a new problem domain, and the tuning process itself often depends on personal experience and intuition in ways that are hard to describe. Since the performance of a given technique depends on both the fundamental quality of the algorithm and the details of its tuning, it can be difficult to determine whether a given technique is genuinely better, or simply better tuned. #R##N#In this work, we propose a meta-modeling approach to support automated hyper parameter optimization, with the goal of providing practical tools to replace hand-tuning with a reproducible and unbiased optimization process. Our approach is to expose the underlying expression graph of how a performance metric (e.g. classification accuracy on validation examples) is computed from parameters that govern not only how individual processing steps are applied, but even which processing steps are included. A hyper parameter optimization algorithm transforms this graph into a program for optimizing that performance metric. Our approach yields state of the art results on three disparate computer vision problems: a face-matching verification task (LFW), a face identification task (PubFig83) and an object recognition task (CIFAR-10), using a single algorithm. More broadly, we argue that the formalization of a meta-model supports more objective, reproducible, and quantitative evaluation of computer vision algorithms, and that it can serve as a valuable tool for guiding algorithm development.

Connected through hyperedge HE-1 (induced by source paper "search to distill pearls are everywhere but not the eyes"):
  with members:
    - "knowledge distillation via route constrained optimization"
    - "towards flops constrained face recognition"
    - "searching for mobilenetv3"
    - "deep high resolution representation learning for visual recognition"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "arcface additive angular margin loss for deep face recognition"
    - "exploring disentangled feature representation beyond face identification"

Connected through hyperedge HE-2 (induced by source paper "adaptive structure constrained robust latent low rank coding for image recovery"):
  with members:
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "subspace clustering by block diagonal representation"
    - "object classification with joint projection and low rank dictionary learning"
    - "fast low rank matrix learning with nonconvex regularization"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (induced by source paper "multi level cnn for lung nodule classification with gaussian process assisted hyperparameter optimization"):
  with members:
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "practical block wise neural network architecture generation"
    - "cma es for hyperparameter optimization of deep neural networks"
    - "transferring rich feature hierarchies for robust visual tracking"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "teacher guided architecture search"):
  with members:
    - "adaptive stochastic natural gradient method for one shot neural architecture search"
    - "massively parallel video networks"
    - "regularized evolution for image classifier architecture search"
    - "irlas inverse reinforcement learning for architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "accelerating neural architecture search using performance prediction"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-5 (induced by source paper "combination of hyperband and bayesian optimization for hyperparameter optimization in deep learning"):
  with members:
    - "ssd single shot multibox detector"
    - "convolutional neural networks with low rank regularization"

Connected through hyperedge HE-6 (induced by source paper "shadho massively scalable hardware aware distributed hyperparameter optimization"):
  with members:
    - "learning transferable architectures for scalable image recognition"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "smash one shot model architecture search through hypernetworks"
    - "end to end learning for self driving cars"
    - "network trimming a data driven neuron pruning approach towards efficient deep architectures"
    - "evaluation system for a bayesian optimization service"
    - "neural architecture search with reinforcement learning"
    - "adanet adaptive structural learning of artificial neural networks"

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)