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 "compressing language models using doped kronecker products"):
  Abstract: Kronecker Products (KP) have been used to compress IoT RNN Applications by 15-38x compression factors, achieving better results than traditional compression methods. However when KP is applied to large Natural Language Processing tasks, it leads to significant accuracy loss (approx 26%). This paper proposes a way to recover accuracy otherwise lost when applying KP to large NLP tasks, by allowing additional degrees of freedom in the KP matrix. More formally, we propose doping, a process of adding an extremely sparse overlay matrix on top of the pre-defined KP structure. We call this compression method doped kronecker product compression. To train these models, we present a new solution to the phenomenon of co-matrix adaption (CMA), which uses a new regularization scheme called co matrix dropout regularization (CMR). We present experimental results that demonstrate compression of a large language model with LSTM layers of size 25 MB by 25x with 1.4% loss in perplexity score. At 25x compression, an equivalent pruned network leads to 7.9% loss in perplexity score, while HMD and LMF lead to 15% and 27% loss in perplexity score respectively.

Includes members:
  Member "compression of recurrent neural networks for efficient language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "restricted recurrent neural networks")
  Member "compressing rnns for iot devices by 15 38x using kronecker products":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "run time efficient rnn compression for inference on edge devices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ternary mobilenets via per layer hybrid filter banks")
  Member "pushing the limits of rnn compression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ternary mobilenets via per layer hybrid filter banks")
  Member "measuring scheduling efficiency of rnns for nlp applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressing rnns for iot devices by 15 38x using kronecker products")
  Member "retraining based iterative weight quantization for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "structured compression by weight encryption for unstructured pruning and quantization")
  Member "deeptwist learning model compression via occasional weight distortion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "structured compression by weight encryption for unstructured pruning and quantization")
  Member "factorization tricks for lstm networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transformer xl attentive language models beyond a fixed length context")

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)