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: "flip rotate pooling convolution and split dropout on convolution neural networks for image classification"
  Abstract: This paper presents a new version of Dropout called Split Dropout (sDropout) and rotational convolution techniques to improve CNNs' performance on image classification. The widely used standard Dropout has advantage of preventing deep neural networks from overfitting by randomly dropping units during training. Our sDropout randomly splits the data into two subsets and keeps both rather than discards one subset. We also introduce two rotational convolution techniques, i.e. rotate-pooling convolution (RPC) and flip-rotate-pooling convolution (FRPC) to boost CNNs' performance on the robustness for rotation transformation. These two techniques encode rotation invariance into the network without adding extra parameters. Experimental evaluations on ImageNet2012 classification task demonstrate that sDropout not only enhances the performance but also converges faster. Additionally, RPC and FRPC make CNNs more robust for rotation transformations. Overall, FRPC together with sDropout bring $1.18\%$ (model of Zeiler and Fergus~\cite{zeiler2013visualizing}, 10-view, top-1) accuracy increase in ImageNet 2012 classification task compared to the original network.

Connected through hyperedge HE-1 (induced by source paper "lgn cnn a biologically inspired cnn architecture"):
  with members:
    - "fundamentals of recurrent neural network rnn and long short term memory lstm network"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "learning rotation invariant convolutional filters for texture classification"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-2 (induced by source paper "non parametric transformation networks"):
  with members:
    - "max margin invariant features from transformed unlabeled data"
    - "dynamic routing between capsules"
    - "deep rotation equivariant network"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "graph based isometry invariant representation learning"
    - "harmonic networks deep translation and rotation equivariance"
    - "learning to extract motion from videos in convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "oriented response networks"):
  with members:
    - "convolutional oriented boundaries"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "identity mappings in deep residual networks"
    - "learning rotation invariant convolutional filters for texture classification"
    - "wide residual networks"
    - "group equivariant convolutional networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "spatial transformer networks"

Connected through hyperedge HE-4 (induced by source paper "deep rotation equivariant network"):
  with members:
    - "rotation equivariant vector field networks"
    - "harmonic networks deep translation and rotation equivariance"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "learning to extract motion from videos in convolutional neural networks"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "group equivariant convolutional networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (induced by source paper "exploiting cyclic symmetry in convolutional neural networks"):
  with members:
    - "learning to extract motion from videos in convolutional neural networks"
    - "group equivariant convolutional networks"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "teaching deep convolutional neural networks to play go"

Connected through hyperedge HE-6 (induced by source paper "learning rotation invariant convolutional filters for texture classification"):
  with members:
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "learning to extract motion from videos in convolutional neural networks"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "load local orientation adaptive descriptor for texture and material classification"
    - "matconvnet convolutional neural networks for matlab"
    - "learning invariant representations with local transformations"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-7 (induced by source paper "gradual dropin of layers to train very deep neural networks"):
  with members:
    - "an empirical evaluation of deep learning on highway driving"
    - "possible mechanisms for neural reconfigurability and their implications"
    - "highway long short term memory rnns for distant speech recognition"
    - "training very deep networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "fitnets hints for thin deep nets"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

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.CV(Computer Vision and Pattern Recognition)