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: "meta learning evolutionary artificial neural networks"
  Abstract: In this paper, we present MLEANN (Meta-Learning Evolutionary Artificial Neural Network), an automatic computational framework for the adaptive optimization of artificial neural networks wherein the neural network architecture, activation function, connection weights; learning algorithm and its parameters are adapted according to the problem. We explored the performance of MLEANN and conventionally designed artificial neural networks for function approximation problems. To evaluate the comparative performance, we used three different well-known chaotic time series. We also present the state of the art popular neural network learning algorithms and some experimentation results related to convergence speed and generalization performance. We explored the performance of backpropagation algorithm; conjugate gradient algorithm, quasi-Newton algorithm and Levenberg-Marquardt algorithm for the three chaotic time series. Performances of the different learning algorithms were evaluated when the activation functions and architecture were changed. We further present the theoretical background, algorithm, design strategy and further demonstrate how effective and inevitable is the proposed MLEANN framework to design a neural network, which is smaller, faster and with a better generalization performance.

Connected through hyperedge HE-1 (induced by source paper "aol adaptive online learning for human trajectory prediction in dynamic video scenes"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "multiple object forecasting predicting future object locations in diverse environments"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "deep online learning via meta learning continual adaptation for model based rl"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "future person localization in first person videos"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"

Connected through hyperedge HE-2 (induced by source paper "born to learn the inspiration progress and future of evolved plastic artificial neural networks"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "evolving deep neural networks"
    - "lstm a search space odyssey"
    - "progressive neural networks"
    - "hierarchical representations for efficient architecture search"
    - "diffusion based neuromodulation can eliminate catastrophic forgetting in simple neural networks"
    - "pathnet evolution channels gradient descent in super neural networks"

Connected through hyperedge HE-3 (induced by source paper "metaheuristic design of feedforward neural networks"):
  with members:
    - "ensemble of heterogeneous flexible neural trees using multiobjective genetic programming"
    - "deep learning in neural networks"
    - "simultaneous optimization of neural network weights and active nodes using metaheuristics"
    - "a brief review of nature inspired algorithms for optimization"
    - "supervised learning in multilayer spiking neural networks"
    - "analysis of a nature inspired firefly algorithm based back propagation neural network training"
    - "on classes of functions for which no free lunch results hold"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "neural networks architecture evaluation in a quantum computer"):
  with members:
    - "metaheuristic design of feedforward neural networks"

Connected through hyperedge HE-5 (induced by source paper "intelligent systems architectures and perspectives"):
  with members:
    - "neuro fuzzy systems sate of the art modeling techniques"
    - "optimization of evolutionary neural networks using hybrid learning algorithms"
    - "evonf a framework for optimization of fuzzy inference systems using neural network learning and evolutionary computation"

Connected through hyperedge HE-6 (induced by source paper "dct based texture classification using soft computing approach"):
  with members:
    - "neuro fuzzy systems sate of the art modeling techniques"

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.AI(Artificial Intelligence)