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: "path level network transformation for efficient architecture search"
  Abstract: We introduce a new function-preserving transformation for efficient neural architecture search. This network transformation allows reusing previously trained networks and existing successful architectures that improves sample efficiency. We aim to address the limitation of current network transformation operations that can only perform layer-level architecture modifications, such as adding (pruning) filters or inserting (removing) a layer, which fails to change the topology of connection paths. Our proposed path-level transformation operations enable the meta-controller to modify the path topology of the given network while keeping the merits of reusing weights, and thus allow efficiently designing effective structures with complex path topologies like Inception models. We further propose a bidirectional tree-structured reinforcement learning meta-controller to explore a simple yet highly expressive tree-structured architecture space that can be viewed as a generalization of multi-branch architectures. We experimented on the image classification datasets with limited computational resources (about 200 GPU-hours), where we observed improved parameter efficiency and better test results (97.70% test accuracy on CIFAR-10 with 14.3M parameters and 74.6% top-1 accuracy on ImageNet in the mobile setting), demonstrating the effectiveness and transferability of our designed architectures.

Connected through hyperedge HE-1 (induced by source paper "autoemb automated embedding dimensionality search in streaming recommendations"):
  with members:
    - "adabert task adaptive bert compression with differentiable neural architecture search"
    - "mixed dimension embeddings with application to memory efficient recommendation systems"
    - "neural input search for large scale recommendation models"
    - "deep learning based recommender system a survey and new perspectives"
    - "regularized evolution for image classifier architecture search"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "neural inheritance relation guided one shot layer assignment search"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "exploring randomly wired neural networks for image recognition"
    - "autogrow automatic layer growing in deep convolutional networks"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "autoslim towards one shot architecture search for channel numbers"
    - "mfas multimodal fusion architecture search"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"

Connected through hyperedge HE-3 (induced by source paper "neural architecture search over decentralized data"):
  with members:
    - "towards federated learning at scale system design"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "deepcache principled cache for mobile deep vision"
    - "federated learning strategies for improving communication efficiency"
    - "leaf a benchmark for federated settings"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "federated learning for mobile keyboard prediction"
    - "a first look at deep learning apps on smartphones"

Connected through hyperedge HE-4 (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"
    - "autodispnet improving disparity estimation with automl"
    - "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"
    - "darts differentiable architecture search"

Connected through hyperedge HE-5 (induced by source paper "evolving robust neural architectures to defend from adversarial attacks"):
  with members:
    - "evolving deep neural networks by multi objective particle swarm optimization for image classification"
    - "one pixel attack for fooling deep neural networks"
    - "evolutionary neural automl for deep learning"
    - "understanding the one pixel attack propagation maps and locality analysis"
    - "model agnostic dual quality assessment for adversarial machine learning and an analysis of current neural networks and defenses"
    - "adversarial patch"
    - "evolutionary architecture search for deep multitask networks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"

Connected through hyperedge HE-6 (induced by source paper "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"):
  with members:
    - "searching for mobilenetv3"
    - "single path one shot neural architecture search with uniform sampling"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "superposition of many models into one"
    - "energy and policy considerations for deep learning in nlp"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-7 (induced by source paper "bayesnas a bayesian approach for neural architecture search"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "snip single shot network pruning based on connection sensitivity"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-8 (induced by source paper "neural architecture refinement a practical way for avoiding overfitting in nas"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "evolutionary neural architecture search for image restoration"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "arcface additive angular margin loss for deep face recognition"
    - "you only search once single shot neural architecture search via direct sparse optimization"
    - "proxylessnas direct neural architecture search on target task and hardware"

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)