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: "compression based bound for non compressed network unified generalization error analysis of large compressible deep neural network"
  Abstract: One of biggest issues in deep learning theory is its generalization ability despite the huge model size. The classical learning theory suggests that overparameterized models cause overfitting. However, practically used large deep models avoid overfitting, which is not well explained by the classical approaches. To resolve this issue, several attempts have been made. Among them, the compression based bound is one of the promising approaches. However, the compression based bound can be applied only to a compressed network, and it is not applicable to the non-compressed original network. In this paper, we give a unified frame-work that can convert compression based bounds to those for non-compressed original networks. The bound gives even better rate than the one for the compressed network by improving the bias term. By establishing the unified frame-work, we can obtain a data dependent generalization error bound which gives a tighter evaluation than the data independent ones.

Connected through hyperedge HE-1 (induced by source paper "dissecting non vacuous generalization bounds based on the mean field approximation"):
  with members:
    - "in defense of uniform convergence generalization via derandomization with an application to interpolating predictors"
    - "hessian based analysis of sgd for deep nets dynamics and generalization"
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "limitations of the empirical fisher approximation"
    - "information theoretic local minima characterization and regularization"
    - "the information complexity of learning tasks their structure and their distance"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"

Connected through hyperedge HE-2 (induced by source paper "scalable deep neural networks via low rank matrix factorization"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "scan a scalable neural networks framework towards compact and efficient models"
    - "universally slimmable networks and improved training techniques"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"
    - "trace norm regularization and faster inference for embedded speech recognition rnns"
    - "theoretical analysis of auto rate tuning by batch normalization"

Connected through hyperedge HE-3 (induced by source paper "domain adaptation regularization for spectral pruning"):
  with members:
    - "accelerating deep unsupervised domain adaptation with transfer channel pruning"
    - "target aware network adaptation for efficient representation learning"
    - "network compression using correlation analysis of layer responses"
    - "domain adaptive deep network compression"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "fine pruning joint fine tuning and compression of a convolutional network with bayesian optimization"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "pruning convolutional neural networks for resource efficient inference"

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)