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: "deep learning for neuroimaging a validation study"
  Abstract: Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.

Connected through hyperedge HE-1 (induced by source paper "fanstore enabling efficient and scalable i o for distributed deep learning"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "imagenet training in minutes"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "tensorlayer a versatile library for efficient deep learning development"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-2 (induced by source paper "residual and plain convolutional neural networks for 3d brain mri classification"):
  with members:
    - "voxresnet deep voxelwise residual networks for volumetric brain segmentation"
    - "identity mappings in deep residual networks"
    - "alzheimer s disease diagnostics by a deeply supervised adaptable 3d convolutional network"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "variational autoencoders for feature detection of magnetic resonance imaging data"):
  with members:
    - "iterative refinement of approximate posterior for training directed belief networks"
    - "nice non linear independent components estimation"
    - "reweighted wake sleep"
    - "neural variational inference and learning in belief networks"

Connected through hyperedge HE-4 (induced by source paper "deep feature learning for eeg recordings"):
  with members:
    - "improving neural networks by preventing co adaptation of feature detectors"

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)