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 "adaptive pruning of neural language models for mobile devices"):
  Abstract: Neural language models (NLMs) exist in an accuracy-efficiency tradeoff space where better perplexity typically comes at the cost of greater computation complexity. In a software keyboard application on mobile devices, this translates into higher power consumption and shorter battery life. This paper represents the first attempt, to our knowledge, in exploring accuracy-efficiency tradeoffs for NLMs. Building on quasi-recurrent neural networks (QRNNs), we apply pruning techniques to provide a "knob" to select different operating points. In addition, we propose a simple technique to recover some perplexity using a negligible amount of memory. Our empirical evaluations consider both perplexity as well as energy consumption on a Raspberry Pi, where we demonstrate which methods provide the best perplexity-power consumption operating point. At one operating point, one of the techniques is able to provide energy savings of 40% over the state of the art with only a 17% relative increase in perplexity.

Includes members:
  Member "fast parametric learning with activation memorization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time aware large kernel convolutions")
  Member "recovering from random pruning on the plasticity of deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pruning filters while training for efficiently optimizing deep learning networks")
  Member "an analysis of neural language modeling at multiple scales":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "an analysis of deep neural network models for practical applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "crl class representative learning for image classification")
  Member "breaking the softmax bottleneck a high rank rnn language model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "on the state of the art of evaluation in neural language models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "dynamic evaluation of neural sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "maxup a simple way to improve generalization of neural network training")
  Member "an experimental analysis of the power consumption of convolutional neural networks for keyword spotting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "keyword spotting for hearing assistive devices robust to external speakers")

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.CL(Computation and Language)