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 "run time efficient rnn compression for inference on edge devices"):
  Abstract: Recurrent neural networks can be large and compute-intensive, yet many applications that benefit from RNNs run on small devices with very limited compute and storage capabilities while still having run-time constraints. As a result, there is a need for compression techniques that can achieve significant compression without negatively impacting inference run-time and task accuracy. This paper explores a new compressed RNN cell implementation called Hybrid Matrix Decomposition (HMD) that achieves this dual objective. This scheme divides the weight matrix into two parts - an unconstrained upper half and a lower half composed of rank-1 blocks. This results in output features where the upper sub-vector has "richer" features while the lower-sub vector has "constrained" features". HMD can compress RNNs by a factor of 2-4x while having a faster run-time than pruning and retaining more model accuracy than matrix factorization. We evaluate this technique on 3 benchmarks.

Includes members:
  Member "compressing rnns for iot devices by 15 38x using kronecker products":
    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 "measuring scheduling efficiency of rnns for nlp applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressing language models using doped kronecker products")
  Member "efficient recurrent neural networks using structured matrices in fpgas":
    also connected through other hyperedges:
      (none)
  Member "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressing rnns for iot devices by 15 38x using kronecker products")
  Member "alternating multi bit quantization for recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quantized neural network inference with precision batching")
  Member "speech commands a dataset for limited vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on feature normalization and data augmentation")
  Member "learning compressed transforms with low displacement rank":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "low memory neural network training a technical report")
  Member "block sparse recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "block wise dynamic sparseness")

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)