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: "evolutionary architecture search for deep multitask networks"
  Abstract: Multitask learning, i.e. learning several tasks at once with the same neural network, can improve performance in each of the tasks. Designing deep neural network architectures for multitask learning is a challenge: There are many ways to tie the tasks together, and the design choices matter. The size and complexity of this problem exceeds human design ability, making it a compelling domain for evolutionary optimization. Using the existing state of the art soft ordering architecture as the starting point, methods for evolving the modules of this architecture and for evolving the overall topology or routing between modules are evaluated in this paper. A synergetic approach of evolving custom routings with evolved, shared modules for each task is found to be very powerful, significantly improving the state of the art in the Omniglot multitask, multialphabet character recognition domain. This result demonstrates how evolution can be instrumental in advancing deep neural network and complex system design in general.

Connected through hyperedge HE-1 (induced by source paper "deeper insights into weight sharing in neural architecture search"):
  with members:
    - "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"
    - "random search and reproducibility for neural architecture search"
    - "weight agnostic neural networks"
    - "nas bench 101 towards reproducible neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "do better imagenet models transfer better"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-2 (induced by source paper "unified multi domain learning and data imputation using adversarial autoencoder"):
  with members:
    - "gain missing data imputation using generative adversarial nets"
    - "handling incomplete heterogeneous data using vaes"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "unified deep supervised domain adaptation and generalization"
    - "vae with a vampprior"
    - "a survey on multi task learning"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "bayesian neural architecture search using a training free performance metric"):
  with members:
    - "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"
    - "investigating recurrent neural network memory structures using neuro evolution"
    - "bin ct urban waste collection based on predicting the container fill level"
    - "low cost recurrent neural network expected performance evaluation"
    - "metaheuristic design of feedforward neural networks"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "branched multi task networks deciding what layers to share"):
  with members:
    - "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"
    - "representation similarity analysis for efficient task taxonomy transfer learning"
    - "feature partitioning for efficient multi task architectures"
    - "attentive single tasking of multiple tasks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "regularized evolution for image classifier architecture search"
    - "taskonomy disentangling task transfer learning"
    - "gnas a greedy neural architecture search method for multi attribute learning"

Connected through hyperedge HE-5 (induced by source paper "multi criterion evolutionary design of deep convolutional neural networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "evolutionary neural automl for deep learning"
    - "searching for a robust neural architecture in four gpu hours"
    - "exploring randomly wired neural networks for image recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-6 (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"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-7 (induced by source paper "adashare learning what to share for efficient deep multi task learning"):
  with members:
    - "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"
    - "deep elastic networks with model selection for multi task learning"
    - "which tasks should be learned together in multi task learning"
    - "branched multi task networks deciding what layers to share"
    - "many task learning with task routing"
    - "taskonomy disentangling task transfer learning"
    - "exploring shared structures and hierarchies for multiple nlp tasks"
    - "spottune transfer learning through adaptive fine tuning"

Connected through hyperedge HE-8 (induced by source paper "routing networks and the challenges of modular and compositional computation"):
  with members:
    - "a meta mdp approach to exploration for lifelong reinforcement learning"
    - "reinforcement learning without backpropagation or a clock"
    - "the termination critic"
    - "modular networks learning to decompose neural computation"
    - "learning abstract options"
    - "modular meta learning"
    - "diversity is all you need learning skills without a reward function"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"

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)