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 neural networks with random gaussian weights a universal classification strategy"
  Abstract: Three important properties of a classification machinery are i) the system preserves the core information of the input data; ii) the training examples convey information about unseen data; and iii) the system is able to treat differently points from different classes. In this paper, we show that these fundamental properties are satisfied by the architecture of deep neural networks. We formally prove that these networks with random Gaussian weights perform a distance-preserving embedding of the data, with a special treatment for in-class and out-of-class data. Similar points at the input of the network are likely to have a similar output. The theoretical analysis of deep networks here presented exploits tools used in the compressed sensing and dictionary learning literature, thereby making a formal connection between these important topics. The derived results allow drawing conclusions on the metric learning properties of the network and their relation to its structure, as well as providing bounds on the required size of the training set such that the training examples would represent faithfully the unseen data. The results are validated with state-of-the-art trained networks.

Connected through hyperedge HE-1 (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"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "untangling in invariant speech recognition"):
  with members:
    - "analyzing learned representations of a deep asr performance prediction model"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"
    - "deep speaker an end to end neural speaker embedding system"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "geodesics of learned representations"
    - "convergent learning do different neural networks learn the same representations"

Connected through hyperedge HE-3 (induced by source paper "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"):
  with members:
    - "a mean field theory of batch normalization"
    - "entropy and mutual information in models of deep neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "exploration by random network distillation"
    - "fisher information and natural gradient learning of random deep networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "large scale study of curiosity driven learning"

Connected through hyperedge HE-4 (induced by source paper "entropy and mutual information in models of deep neural networks"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "the stochastic interpolation method a simple scheme to prove replica formulas in bayesian inference"
    - "the layered structure of tensor estimation and its mutual information"
    - "multi layer generalized linear estimation"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "opening the black box of deep neural networks via information"
    - "inference in deep networks in high dimensions"
    - "estimating mixture entropy with pairwise distances"

Connected through hyperedge HE-5 (induced by source paper "artistic domain generalisation methods are limited by their deep representations"):
  with members:
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning to generalize meta learning for domain generalization"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "deeper broader and artier domain generalization"
    - "generalisation and sharing in triplet convnets for sketch based visual search"
    - "domain separation networks"
    - "domain generalization for object recognition with multi task autoencoders"
    - "detecting people in cubist art"

Connected through hyperedge HE-6 (induced by source paper "from bow to cnn two decades of texture representation for texture classification"):
  with members:
    - "deep learning for generic object detection a survey"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "low rank bilinear pooling for fine grained classification"
    - "deep ten texture encoding network"
    - "approximate fisher kernels of non iid image models for image categorization"
    - "using filter banks in convolutional neural networks for texture classification"

Connected through hyperedge HE-7 (induced by source paper "intriguing properties of randomly weighted networks generalizing while learning next to nothing"):
  with members:
    - "fix your classifier the marginal value of training the last weight layer"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "network dissection quantifying interpretability of deep visual representations"
    - "incremental learning through deep adaptation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning discrete weights using the local reparameterization trick"

Connected through hyperedge HE-8 (induced by source paper "measuring and regularizing networks in function space"):
  with members:
    - "continual learning through synaptic intelligence"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "gradient episodic memory for continual learning"
    - "regularizing neural networks by penalizing confident output distributions"
    - "icarl incremental classifier and representation learning"
    - "overcoming catastrophic forgetting in neural networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)