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 "rtmobile beyond real time mobile acceleration of rnns for speech recognition"):
  Abstract: Recurrent neural networks (RNNs) based automatic speech recognition has nowadays become prevalent on mobile devices such as smart phones. However, previous RNN compression techniques either suffer from hardware performance overhead due to irregularity or significant accuracy loss due to the preserved regularity for hardware friendliness. In this work, we propose RTMobile that leverages both a novel block-based pruning approach and compiler optimizations to accelerate RNN inference on mobile devices. Our proposed RTMobile is the first work that can achieve real-time RNN inference on mobile platforms. Experimental results demonstrate that RTMobile can significantly outperform existing RNN hardware acceleration methods in terms of inference accuracy and time. Compared with prior work on FPGA, RTMobile using Adreno 640 embedded GPU on GRU can improve the energy-efficiency by about 40$\times$ while maintaining the same inference time.

Includes members:
  Member "deepsz a novel framework to compress deep neural networks by using error bounded lossy compression":
    also connected through other hyperedges:
      (none)
  Member "protecting neural networks with hierarchical random switching towards better robustness accuracy trade off for stochastic defenses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "advms a multi source multi cost defense against adversarial attacks")
  Member "2pfpce two phase filter pruning based on conditional entropy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an image enhancing pattern based sparsity for real time inference on mobile devices")
  Member "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning")
  Member "defensive dropout for hardening deep neural networks under adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards query efficient black box adversary with zeroth order natural gradient descent")
  Member "systematic weight pruning of dnns using alternating direction method of multipliers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning")
  Member "soft filter pruning for accelerating deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "e rnn design optimization for efficient recurrent neural networks in fpgas":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards query efficient black box adversary with zeroth order natural gradient descent")

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.SD(Sound)