human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "dynamically hierarchy revolution dirnet for compressing recurrent neural network on mobile devices"
  Abstract: Recurrent neural networks (RNNs) achieve cutting-edge performance on a variety of problems. However, due to their high computational and memory demands, deploying RNNs on resource constrained mobile devices is a challenging task. To guarantee minimum accuracy loss with higher compression rate and driven by the mobile resource requirement, we introduce a novel model compression approach DirNet based on an optimized fast dictionary learning algorithm, which 1) dynamically mines the dictionary atoms of the projection dictionary matrix within layer to adjust the compression rate 2) adaptively changes the sparsity of sparse codes cross the hierarchical layers. Experimental results on language model and an ASR model trained with a 1000h speech dataset demonstrate that our method significantly outperforms prior approaches. Evaluated on off-the-shelf mobile devices, we are able to reduce the size of original model by eight times with real-time model inference and negligible accuracy loss.

Connected through hyperedge HE-1 (induced by source paper "mobivsr a visual speech recognition solution for mobile devices"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "model compression via distillation and quantization"
    - "learning motion in feature space locally consistent deformable convolution networks for fine grained action detection"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "harnessing ai for speech reconstruction using multi view silent video feed"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "3d depthwise convolution reducing model parameters in 3d vision tasks"
    - "improved speech reconstruction from silent video"

Connected through hyperedge HE-2 (induced by source paper "generative visual dialogue system via adaptive reasoning and weighted likelihood estimation"):
  with members:
    - "accelerating proposal generation network for fast face detection on mobile devices"
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "flipdial a generative model for two way visual dialogue"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "focal loss for dense object detection"
    - "are you talking to me reasoned visual dialog generation through adversarial learning"
    - "adversarial learning for neural dialogue generation"
    - "best of both worlds transferring knowledge from discriminative learning to a generative visual dialog model"

Connected through hyperedge HE-3 (induced by source paper "micik mining cross layer inherent similarity knowledge for deep model compression"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "exploring the regularity of sparse structure in convolutional neural networks"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "channel pruning for accelerating very deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Question: Please tell me which class the center node 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)