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: "neural architecture construction using envelopenets"
  Abstract: In recent years, advances in the design of convolutional neural networks have resulted in significant improvements on the image classification and object detection problems. One of the advances is networks built by stacking complex cells, seen in such networks as InceptionNet and NasNet. These cells are either constructed by hand, generated by generative networks or discovered by search. Unlike conventional networks (where layers consist of a convolution block, sampling and non linear unit), the new cells feature more complex designs consisting of several filters and other operators connected in series and parallel. Recently, several cells have been proposed or generated that are supersets of previously proposed custom or generated cells. Influenced by this, we introduce a network construction method based on EnvelopeNets. An EnvelopeNet is a deep convolutional neural network of stacked EnvelopeCells. EnvelopeCells are supersets (or envelopes) of previously proposed handcrafted and generated cells. We propose a method to construct improved network architectures by restructuring EnvelopeNets. The algorithm restructures an EnvelopeNet by rearranging blocks in the network. It identifies blocks to be restructured using metrics derived from the featuremaps collected during a partial training run of the EnvelopeNet. The method requires less computation resources to generate an architecture than an optimized architecture search over the entire search space of blocks. The restructured networks have higher accuracy on the image classification problem on a representative dataset than both the generating EnvelopeNet and an equivalent arbitrary network.

Connected through hyperedge HE-1 (induced by source paper "eena efficient evolution of neural architecture"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "dropblock a regularization method for convolutional networks"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "path level network transformation for efficient architecture search"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-2 (induced by source paper "nasib neural architecture search within budget"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "a survey on neural architecture search"
    - "instanas instance aware neural architecture search"
    - "reinforced evolutionary neural architecture search"
    - "graph hypernetworks for neural architecture search"
    - "you only search once single shot neural architecture search via direct sparse optimization"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"

Connected through hyperedge HE-3 (induced by source paper "proxylessnas direct neural architecture search on target task and hardware"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "neural architecture optimization"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "path level network transformation 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.NE(Neural and Evolutionary Computing)