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: "degrees of freedom in deep neural networks"
  Abstract: In this paper, we explore degrees of freedom in deep sigmoidal neural networks. We show that the degrees of freedom in these models is related to the expected optimism, which is the expected difference between test error and training error. We provide an efficient Monte-Carlo method to estimate the degrees of freedom for multi-class classification methods. We show degrees of freedom are lower than the parameter count in a simple XOR network. We extend these results to neural nets trained on synthetic and real data, and investigate impact of network's architecture and different regularization choices. The degrees of freedom in deep networks are dramatically smaller than the number of parameters, in some real datasets several orders of magnitude. Further, we observe that for fixed number of parameters, deeper networks have less degrees of freedom exhibiting a regularization-by-depth.

Connected through hyperedge HE-1 (induced by source paper "mapping motor cortex stimulation to muscle responses a deep neural network modeling approach"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "sensor based estimation of dim light melatonin onset dlmo using features of two time scales"):
  with members:
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "diversity and degrees of freedom in regression ensembles"):
  with members:
    - "all you need is a good init"
    - "deep learning in neural networks"
    - "modular autoencoders for ensemble feature extraction"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "practical recommendations for gradient based training of deep architectures"
    - "representation learning a review and new perspectives"
    - "multi target regression via input space expansion treating targets as inputs"

Connected through hyperedge HE-4 (induced by source paper "systematic evaluation of convolution neural network advances on the imagenet"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "on architectural choices in deep learning from network structure to gradient convergence and parameter estimation"
    - "disturblabel regularizing cnn on the loss layer"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"

Connected through hyperedge HE-5 (induced by source paper "quicksilver fast predictive image registration a deep learning approach"):
  with members:
    - "a deep metric for multimodal registration"
    - "fast predictive image registration"
    - "flownet learning optical flow with convolutional networks"
    - "adam a method for stochastic optimization"
    - "striving for simplicity the all convolutional net"
    - "fully convolutional networks for semantic segmentation"

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)