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 "antman sparse low rank compression to accelerate rnn inference"):
  Abstract: Wide adoption of complex RNN based models is hindered by their inference performance, cost and memory requirements. To address this issue, we develop AntMan, combining structured sparsity with low-rank decomposition synergistically, to reduce model computation, size and execution time of RNNs while attaining desired accuracy. AntMan extends knowledge distillation based training to learn the compressed models efficiently. Our evaluation shows that AntMan offers up to 100x computation reduction with less than 1pt accuracy drop for language and machine reading comprehension models. Our evaluation also shows that for a given accuracy target, AntMan produces 5x smaller models than the state-of-art. Lastly, we show that AntMan offers super-linear speed gains compared to theoretical speedup, demonstrating its practical value on commodity hardware.

Includes members:
  Member "slimnets an exploration of deep model compression and acceleration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "positive unlabeled compression on the cloud")
  Member "retraining based iterative weight quantization for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "structured compression by weight encryption for unstructured pruning and quantization")
  Member "shufflenet an extremely efficient convolutional neural network for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "learning intrinsic sparse structures within long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fully quantized transformer for improved translation")
  Member "regularizing and optimizing lstm language models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "exploring sparsity in recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "soft threshold weight reparameterization for learnable sparsity")
  Member "a survey of model compression and acceleration for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")
  Member "learning compact recurrent neural networks with block term tensor decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gate decorator global filter pruning method for accelerating deep convolutional neural networks")

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)