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: "reducing the model order of deep neural networks using information theory"
  Abstract: Deep neural networks are typically represented by a much larger number of parameters than shallow models, making them prohibitive for small footprint devices. Recent research shows that there is considerable redundancy in the parameter space of deep neural networks. In this paper, we propose a method to compress deep neural networks by using the Fisher Information metric, which we estimate through a stochastic optimization method that keeps track of second-order information in the network. We first remove unimportant parameters and then use non-uniform fixed point quantization to assign more bits to parameters with higher Fisher Information estimates. We evaluate our method on a classification task with a convolutional neural network trained on the MNIST data set. Experimental results show that our method outperforms existing methods for both network pruning and quantization.

Connected through hyperedge HE-1 (induced by source paper "trends and advancements in deep neural network communication"):
  with members:
    - "deepcabac a universal compression algorithm for deep neural networks"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "advances and open problems in federated learning"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "improving federated learning personalization via model agnostic meta learning"
    - "hierarchical federated learning across heterogeneous cellular networks"

Connected through hyperedge HE-2 (induced by source paper "deepcabac a universal compression algorithm for deep neural networks"):
  with members:
    - "robust and communication efficient federated learning from non iid data"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "scalable methods for 8 bit training of neural networks"
    - "compact and computationally efficient representation of deep neural networks"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "training deep neural networks with 8 bit floating point numbers"
    - "learning sparse neural networks via sensitivity driven regularization"
    - "universal deep neural network compression"

Connected through hyperedge HE-3 (induced by source paper "dimensionality compression and expansion in deep neural networks"):
  with members:
    - "geometry of deep generative models for disentangled representations"
    - "the local dimension of deep manifold"
    - "energy entropy competition and the effectiveness of stochastic gradient descent in machine learning"
    - "understanding convolutional neural networks with information theory an initial exploration"
    - "fisher rao metric geometry and complexity of neural networks"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "the riemannian geometry of deep generative models"
    - "a bayesian perspective on generalization and stochastic gradient descent"

Connected through hyperedge HE-4 (induced by source paper "bitnet bit regularized deep neural networks"):
  with members:
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "mixed low precision deep learning inference using dynamic fixed point"
    - "alternating direction method of multipliers for sparse convolutional neural networks"
    - "fixed point optimization of deep neural networks with adaptive step size retraining"
    - "two bit networks for deep learning on resource constrained embedded devices"
    - "towards the limit of network quantization"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"

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)