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: "how far can we go without convolution improving fully connected networks"
  Abstract: We propose ways to improve the performance of fully connected networks. We found that two approaches in particular have a strong effect on performance: linear bottleneck layers and unsupervised pre-training using autoencoders without hidden unit biases. We show how both approaches can be related to improving gradient flow and reducing sparsity in the network. We show that a fully connected network can yield approximately 70% classification accuracy on the permutation-invariant CIFAR-10 task, which is much higher than the current state-of-the-art. By adding deformations to the training data, the fully connected network achieves 78% accuracy, which is just 10% short of a decent convolutional network.

Connected through hyperedge HE-1 (induced by source paper "exploring the back alleys analysing the robustness of alternative neural network architectures against adversarial attacks"):
  with members:
    - "adversarial attacks on remote user authentication using behavioural mouse dynamics"
    - "a novel method for extracting interpretable knowledge from a spiking neural classifier with time varying synaptic weights"
    - "a comprehensive analysis on adversarial robustness of spiking neural networks"
    - "improving black box adversarial attacks with a transfer based prior"
    - "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks"
    - "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "combinatorial attacks on binarized neural networks"

Connected through hyperedge HE-2 (induced by source paper "biologically plausible deep learning but how far can we go with shallow networks"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "deep learning in spiking neural networks"
    - "putting a bug in ml the moth olfactory network learns to read mnist"
    - "error forward propagation reusing feedforward connections to propagate errors in deep learning"
    - "feedback alignment in deep convolutional networks"
    - "regularized evolution for image classifier architecture search"
    - "a biologically plausible learning rule for deep learning in the brain"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"

Connected through hyperedge HE-3 (induced by source paper "deepmimic mentor student unlabeled data based training"):
  with members:
    - "stealing knowledge from protected deep neural networks using composite unlabeled data"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "robust student network learning"
    - "learning transferable architectures for scalable image recognition"
    - "low shot learning with imprinted weights"
    - "mixup beyond empirical risk minimization"
    - "squeeze and excitation networks"

Connected through hyperedge HE-4 (induced by source paper "classifying signals on irregular domains via convolutional cluster pooling"):
  with members:
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "robust spatial filtering with graph convolutional neural networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-5 (induced by source paper "variational information distillation for knowledge transfer"):
  with members:
    - "mine mutual information neural estimation"
    - "adversarial network compression"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "learning without forgetting"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-6 (induced by source paper "adversarial reprogramming of neural networks"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "adversarial logit pairing"
    - "adversarial attacks on neural network policies"
    - "robust physical world attacks on deep learning models"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-7 (induced by source paper "matching convolutional neural networks without priors about data"):
  with members:
    - "topology adaptive graph convolutional networks"
    - "convolutional neural networks on irregular domains through approximate translations on inferred graphs"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "neural message passing for quantum chemistry"
    - "motif based convolutional neural network on graphs"
    - "geometric deep learning going beyond euclidean data"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "a neighborhood preserving translation operator on graphs"

Connected through hyperedge HE-8 (induced by source paper "the loss surface of deep and wide neural networks"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "eigenvalues of the hessian in deep learning singularity and beyond"
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "theory ii landscape of the empirical risk in deep learning"
    - "understanding deep learning requires rethinking generalization"
    - "globally optimal training of generalized polynomial neural networks with nonlinear spectral methods"
    - "piecewise convexity of artificial neural networks"

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.LG(Machine Learning)