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 network ensembles evaluation of aggregation algorithms"
  Abstract: Ensembles of artificial neural networks show improved generalization capabilities that outperform those of single networks. However, for aggregation to be effective, the individual networks must be as accurate and diverse as possible. An important problem is, then, how to tune the aggregate members in order to have an optimal compromise between these two conflicting conditions. We present here an extensive evaluation of several algorithms for ensemble construction, including new proposals and comparing them with standard methods in the literature. We also discuss a potential problem with sequential aggregation algorithms: the non-frequent but damaging selection through their heuristics of particularly bad ensemble members. We introduce modified algorithms that cope with this problem by allowing individual weighting of aggregate members. Our algorithms and their weighted modifications are favorably tested against other methods in the literature, producing a sensible improvement in performance on most of the standard statistical databases used as benchmarks.

Connected through hyperedge HE-1 (induced by source paper "mothernets rapid deep ensemble learning"):
  with members:
    - "self training ensemble networks for zero shot image recognition"
    - "efficient architecture search by network transformation"
    - "snapshot ensembles train 1 get m for free"
    - "less is more a comprehensive framework for the number of components of ensemble classifiers"
    - "deep networks with stochastic depth"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "learning without forgetting"
    - "wide residual networks"

Connected through hyperedge HE-2 (induced by source paper "rapid training of very large ensembles of diverse neural networks"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep mutual learning"
    - "snapshot ensembles train 1 get m for free"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "modularized morphing of neural networks"
    - "less is more a comprehensive framework for the number of components of ensemble classifiers"
    - "deep networks with stochastic depth"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "distributed learning of deep neural network over multiple agents"):
  with members:
    - "revisiting distributed synchronous sgd"
    - "communication efficient learning of deep networks from decentralized data"
    - "context encoders feature learning by inpainting"
    - "inverting visual representations with convolutional networks"
    - "deep residual learning for image recognition"
    - "qualitatively characterizing neural network optimization problems"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

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)