human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "powersgd practical low rank gradient compression for distributed optimization"):
  Abstract: We study gradient compression methods to alleviate the communication bottleneck in data-parallel distributed optimization. Despite the significant attention received, current compression schemes either do not scale well or fail to achieve the target test accuracy. We propose a new low-rank gradient compressor based on power iteration that can i) compress gradients rapidly, ii) efficiently aggregate the compressed gradients using all-reduce, and iii) achieve test performance on par with SGD. The proposed algorithm is the only method evaluated that achieves consistent wall-clock speedups when benchmarked against regular SGD with an optimized communication backend. We demonstrate reduced training times for convolutional networks as well as LSTMs on common datasets. Our code is available at this https URL.

Includes members:
  Member "fairseq a fast extensible toolkit for sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "error feedback fixes signsgd and other gradient compression schemes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "the error feedback framework better rates for sgd with delayed gradients and compressed communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "signsgd with majority vote is communication efficient and fault tolerant":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "threats to federated learning a survey")
  Member "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters")
  Member "detecting memorization in relu networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards quantifying intrinsic generalization of deep relu networks")
  Member "gradiveq vector quantization for bandwidth efficient gradient aggregation in distributed cnn training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "stronger generalization bounds for deep nets via a compression approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")

Question: Please tell me which class the source paper 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)