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: "efficient neural architecture search via proximal iterations"
  Abstract: Neural architecture search (NAS) recently attracts much research attention because of its ability to identify better architectures than handcrafted ones. However, many NAS methods, which optimize the search process in a discrete search space, need many GPU days for convergence. Recently, DARTS, which constructs a differentiable search space and then optimizes it by gradient descent, can obtain high-performance architecture and reduces the search time to several days. However, DARTS is still slow as it updates an ensemble of all operations and keeps only one after convergence. Besides, DARTS can converge to inferior architectures due to the strong correlation among operations. In this paper, we propose a new differentiable Neural Architecture Search method based on Proximal gradient descent (denoted as NASP). Different from DARTS, NASP reformulates the search process as an optimization problem with a constraint that only one operation is allowed to be updated during forward and backward propagation. Since the constraint is hard to deal with, we propose a new algorithm inspired by proximal iterations to solve it. Experiments on various tasks demonstrate that NASP can obtain high-performance architectures with 10 times of speedup on the computational time than DARTS.

Connected through hyperedge HE-1 (induced by source paper "efficient backbone search for scene text recognition"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "detnas backbone search for object detection"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "scene text recognition from two dimensional perspective"

Connected through hyperedge HE-2 (induced by source paper "stabilizing differentiable architecture search via perturbation based regularization"):
  with members:
    - "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"
    - "understanding architectures learnt by cell based neural architecture search"
    - "certified adversarial robustness via randomized smoothing"
    - "searching for a robust neural architecture in four gpu hours"
    - "fair darts eliminating unfair advantages in differentiable architecture search"
    - "understanding and robustifying differentiable architecture search"
    - "bayesnas a bayesian approach for neural architecture search"
    - "darts improved differentiable architecture search with early stopping"

Connected through hyperedge HE-3 (induced by source paper "darts improved differentiable architecture search with early stopping"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "exploring randomly wired neural networks for image recognition"
    - "neural input search for large scale recommendation models"
    - "xnas neural architecture search with expert advice"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "searching to exploit memorization effect in learning from corrupted labels"):
  with members:
    - "adaptive stochastic natural gradient method for one shot neural architecture search"
    - "how does disagreement help generalization against label corruption"
    - "searching for interaction functions in collaborative filtering"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "joint optimization framework for learning with noisy labels"
    - "taking human out of learning applications a survey on automated machine learning"
    - "dimensionality driven learning with noisy labels"
    - "co teaching robust training of deep neural networks with extremely noisy labels"

Connected through hyperedge HE-5 (induced by source paper "searching for interaction functions in collaborative filtering"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "taking human out of learning applications a survey on automated machine learning"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "accelerated and inexact soft impute for large scale matrix and tensor completion"
    - "neural collaborative filtering"

Connected through hyperedge HE-6 (induced by source paper "efficient neural interaction function search for collaborative filtering"):
  with members:
    - "autokge searching scoring functions for knowledge graph embedding"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "taking human out of learning applications a survey on automated machine learning"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "accelerated and inexact soft impute for large scale matrix and tensor completion"
    - "neural collaborative filtering"

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)