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: "dvolver efficient pareto optimal neural network architecture search"
  Abstract: Automatic search of neural network architectures is a standing research topic. In addition to the fact that it presents a faster alternative to hand-designed architectures, it can improve their efficiency and for instance generate Convolutional Neural Networks (CNN) adapted for mobile devices. In this paper, we present a multi-objective neural architecture search method to find a family of CNN models with the best accuracy and computational resources tradeoffs, in a search space inspired by the state-of-the-art findings in neural search. Our work, called Dvolver, evolves a population of architectures and iteratively improves an approximation of the optimal Pareto front. Applying Dvolver on the model accuracy and on the number of floating points operations as objective functions, we are able to find, in only 2.5 days, a set of competitive mobile models on ImageNet. Amongst these models one architecture has the same Top-1 accuracy on ImageNet as NASNet-A mobile with 8% less floating point operations and another one has a Top-1 accuracy of 75.28% on ImageNet exceeding by 0.28% the best MobileNetV2 model for the same computational resources.

Connected through hyperedge HE-1 (induced by source paper "memnet memory efficiency guided neural architecture search with augment trim learning"):
  with members:
    - "ktan knowledge transfer adversarial network"
    - "you only search once single shot neural architecture search via direct sparse optimization"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "wearable audio and imu based shot detection in racquet sports"
    - "efficient neural architecture search via parameter sharing"
    - "evolving deep neural networks"
    - "progressive neural architecture search"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-2 (induced by source paper "pabo pseudo agent based multi objective bayesian hyperparameter optimization for efficient neural accelerator design"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "in datacenter performance analysis of a tensor processing unit"
    - "trannsformer n eural n etwork transform ation for memristive crossbar based neuromorphic system design"
    - "neural networks designing neural networks multi objective hyper parameter optimization"
    - "cma es for hyperparameter optimization of deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "constrained multi objective optimization for automated machine learning"):
  with members:
    - "a multi objective direct algorithm toward structural damage identification with limited dynamic response information"
    - "big models for big data using multi objective averaged one dependence estimators"

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)