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: "measuring scheduling efficiency of rnns for nlp applications"
  Abstract: Recurrent neural networks (RNNs) have shown state of the art results for speech recognition, natural language processing, image captioning and video summarizing applications. Many of these applications run on low-power platforms, so their energy efficiency is extremely important. We observed that cache-oblivious RNN scheduling during inference typically results in 30-50x more data transferred on and off the CPU than the application's working set size. This can potentially impact its energy efficiency. This paper presents a new metric called Data Reuse Efficiency to gauge the RNN scheduling efficiency of a platform and shows the factors that influence the DRE value. Additionally, this paper discusses an optimization to improve reuse in RNNs and highlights the positive impact of this optimization on the total amount of memory read from or written to the memory controller (and, hence, the DRE value) during the execution of an RNN application for a mobile SoC.

Connected through hyperedge HE-1 (induced by source paper "compressing language models using doped kronecker products"):
  with members:
    - "compression of recurrent neural networks for efficient language modeling"
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "run time efficient rnn compression for inference on edge devices"
    - "pushing the limits of rnn compression"
    - "retraining based iterative weight quantization for deep neural networks"
    - "deeptwist learning model compression via occasional weight distortion"
    - "factorization tricks for lstm networks"
    - "compressing recurrent neural network with tensor train"

Connected through hyperedge HE-2 (induced by source paper "compressing rnns for iot devices by 15 38x using kronecker products"):
  with members:
    - "tucker tensor layer in fully connected neural networks"
    - "run time efficient rnn compression for inference on edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "the state of sparsity in deep neural networks"
    - "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "block sparse recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "run time efficient rnn compression for inference on edge devices"):
  with members:
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "efficient recurrent neural networks using structured matrices in fpgas"
    - "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics"
    - "alternating multi bit quantization for recurrent neural networks"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "learning compressed transforms with low displacement rank"
    - "block sparse recurrent neural networks"
    - "breaking the softmax bottleneck a high rank rnn language model"

Connected through hyperedge HE-4 (induced by source paper "pushing the limits of rnn compression"):
  with members:
    - "ternary hybrid neural tree networks for highly constrained iot applications"
    - "run time efficient rnn compression for inference on edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "learning compressed transforms with low displacement rank"
    - "hello edge keyword spotting on microcontrollers"
    - "factorization tricks for lstm networks"
    - "deep convolutional and recurrent models for human activity recognition using wearables"

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.DC(Distributed, Parallel, and Cluster Computing)