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 "rethinking full connectivity in recurrent neural networks"):
  Abstract: Recurrent neural networks (RNNs) are omnipresent in sequence modeling tasks. Practical models usually consist of several layers of hundreds or thousands of neurons which are fully connected. This places a heavy computational and memory burden on hardware, restricting adoption in practical low-cost and low-power devices. Compared to fully convolutional models, the costly sequential operation of RNNs severely hinders performance on parallel hardware. This paper challenges the convention of full connectivity in RNNs. We study structurally sparse RNNs, showing that they are well suited for acceleration on parallel hardware, with a greatly reduced cost of the recurrent operations as well as orders of magnitude less recurrent weights. Extensive experiments on challenging tasks ranging from language modeling and speech recognition to video action recognition reveal that structurally sparse RNNs achieve competitive performance as compared to fully-connected networks. This allows for using large sparse RNNs for a wide range of real-world tasks that previously were too costly with fully connected networks.

Includes members:
  Member "independently recurrent neural network indrnn building a longer and deeper rnn":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "campfire compressible regularization free structured sparse training for hardware accelerators")
  Member "sparse persistent rnns squeezing large recurrent networks on chip":
    also connected through other hyperedges:
      (none)
  Member "efficient neural audio synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "what is the state of neural network pruning")
  Member "an analysis of neural language modeling at multiple scales":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "predefined sparseness in recurrent sequence models":
    also connected through other hyperedges:
      (none)
  Member "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blank language models")
  Member "block sparse recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)