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: "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
  Abstract: Training deep neural networks results in strong learned representations that show good generalization capabilities. In most cases, training involves iterative modification of all weights inside the network via back-propagation. In Extreme Learning Machines, it has been suggested to set the first layer of a network to fixed random values instead of learning it. In this paper, we propose to take this approach a step further and fix almost all layers of a deep convolutional neural network, allowing only a small portion of the weights to be learned. As our experiments show, fixing even the majority of the parameters of the network often results in performance which is on par with the performance of learning all of them. The implications of this intriguing property of deep neural networks are discussed and we suggest ways to harness it to create more robust representations.

Connected through hyperedge HE-1 (induced by source paper "training batchnorm and only batchnorm on the expressive power of random features in cnns"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "a mean field theory of batch normalization"
    - "what s hidden in a randomly weighted neural network"
    - "on the power and limitations of random features for understanding neural networks"
    - "towards understanding regularization in batch normalization"
    - "understanding batch normalization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "the shattered gradients problem if resnets are the answer then what is the question"

Connected through hyperedge HE-2 (induced by source paper "deep randomized neural networks"):
  with members:
    - "richness of deep echo state network dynamics"
    - "what s hidden in a randomly weighted neural network"
    - "comparison between deepesns and gated rnns on multivariate time series prediction"
    - "deep semi random features for nonlinear function approximation"
    - "stochastic configuration networks fundamentals and algorithms"
    - "multiplex visibility graphs to investigate recurrent neural network dynamics"
    - "deep stochastic configuration networks with universal approximation property"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"

Connected through hyperedge HE-3 (induced by source paper "the hsic bottleneck deep learning without back propagation"):
  with members:
    - "on mutual information maximization for representation learning"
    - "error forward propagation reusing feedforward connections to propagate errors in deep learning"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "feedback alignment in deep convolutional networks"
    - "a biologically plausible learning rule for deep learning in the brain"
    - "gradient target propagation"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "dependency bottleneck in auto encoding architectures an empirical study"

Connected through hyperedge HE-4 (induced by source paper "capacity allocation analysis of neural networks a tool for principled architecture design"):
  with members:
    - "capacity allocation through neural network layers"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "deepbase deep inspection of neural networks"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "efficient neural audio synthesis"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

Connected through hyperedge HE-5 (induced by source paper "j net randomly weighted u net for audio source separation"):
  with members:
    - "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation"
    - "progressive image deraining networks a better and simpler baseline"
    - "convolutional recurrent neural network based progressive learning for monaural speech enhancement"
    - "audio denoising with deep network priors"
    - "randomly weighted cnns for music audio classification"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "deep image prior"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "capacity allocation through neural network layers"):
  with members:
    - "capacity allocation analysis of neural networks a tool for principled architecture design"
    - "deepbase deep inspection of neural networks"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "efficient neural audio synthesis"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "samplernn an unconditional end to end neural audio generation model"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-7 (induced by source paper "randomly weighted cnns for music audio classification"):
  with members:
    - "one deep music representation to rule them all a comparative analysis of different representation learning strategies"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "deep image prior"
    - "dlr toward a deep learned rhythmic representation for music content analysis"
    - "look listen and learn"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "multi label music genre classification from audio text and images using deep features"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"

Connected through hyperedge HE-8 (induced by source paper "predicting adversarial examples with high confidence"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "high dimensional spaces deep learning and adversarial examples"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "on calibration of modern neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "countering adversarial images using input transformations"

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)