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: "peephole predicting network performance before training"
  Abstract: The quest for performant networks has been a significant force that drives the advancements of deep learning in recent years. While rewarding, improving network design has never been an easy journey. The large design space combined with the tremendous cost required for network training poses a major obstacle to this endeavor. In this work, we propose a new approach to this problem, namely, predicting the performance of a network before training, based on its architecture. Specifically, we develop a unified way to encode individual layers into vectors and bring them together to form an integrated description via LSTM. Taking advantage of the recurrent network's strong expressive power, this method can reliably predict the performances of various network architectures. Our empirical studies showed that it not only achieved accurate predictions but also produced consistent rankings across datasets -- a key desideratum in performance prediction.

Connected through hyperedge HE-1 (induced by source paper "econas finding proxies for economical neural architecture search"):
  with members:
    - "detnas backbone search for object detection"
    - "searching for mobilenetv3"
    - "nas fcos fast neural architecture search for object detection"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "are all training examples created equal an empirical study"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-2 (induced by source paper "arctext an unified text approach to describing convolutional neural network architectures"):
  with members:
    - "automatically designing cnn architectures using genetic algorithm for image classification"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "neunets an automated synthesis engine for neural network design"):
  with members:
    - "alphax exploring neural architectures with deep neural networks and monte carlo tree search"
    - "practical deep learning architecture optimization"
    - "massively parallel hyperparameter tuning"
    - "regularized evolution for image classifier architecture search"
    - "incremental training of deep convolutional neural networks"
    - "efficient image dataset classification difficulty estimation for predicting deep learning accuracy"
    - "darts differentiable architecture search"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"

Connected through hyperedge HE-4 (induced by source paper "bananas bayesian optimization with neural architectures for neural architecture search"):
  with members:
    - "probo a framework for using probabilistic programming in bayesian optimization"
    - "multi objective neural architecture search via predictive network performance optimization"
    - "random search and reproducibility for neural architecture search"
    - "probo versatile bayesian optimization using any probabilistic programming language"
    - "sample efficient neural architecture search by learning action space"
    - "d vae a variational autoencoder for directed acyclic graphs"
    - "nas bench 101 towards reproducible neural architecture search"
    - "bayesnas a bayesian approach for neural architecture search"

Connected through hyperedge HE-5 (induced by source paper "feature partitioning for efficient multi task architectures"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "multi task zipping via layer wise neuron sharing"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "combating catastrophic forgetting with developmental compression"
    - "unifying and merging well trained deep neural networks for inference stage"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "evolutionary architecture search for deep multitask networks"

Connected through hyperedge HE-6 (induced by source paper "rnas architecture ranking for powerful networks"):
  with members:
    - "cars continuous evolution for efficient neural architecture search"
    - "nas bench 101 towards reproducible neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "co evolutionary compression for unpaired image translation"
    - "searching for accurate binary neural architectures"
    - "regularized evolution for image classifier architecture search"
    - "neural architecture optimization"
    - "darts differentiable architecture search"

Connected through hyperedge HE-7 (induced by source paper "fast task aware architecture inference"):
  with members:
    - "deep sets"
    - "darts differentiable architecture search"
    - "tapas train less accuracy predictor for architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "transfer learning with neural automl"
    - "accelerating neural architecture search using performance prediction"
    - "progressive neural architecture search"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-8 (induced by source paper "you might also like this model data driven approach for recommending deep learning models for unknown image datasets"):
  with members:
    - "do better imagenet models transfer better"
    - "regularized evolution for image classifier architecture search"
    - "efficient image dataset classification difficulty estimation for predicting deep learning accuracy"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "accelerating neural architecture search using performance prediction"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "hierarchical representations for efficient 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)