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: "fpga based low power speech recognition with recurrent neural networks"
  Abstract: In this paper, a neural network based real-time speech recognition (SR) system is developed using an FPGA for very low-power operation. The implemented system employs two recurrent neural networks (RNNs); one is a speech-to-character RNN for acoustic modeling (AM) and the other is for character-level language modeling (LM). The system also employs a statistical word-level LM to improve the recognition accuracy. The results of the AM, the character-level LM, and the word-level LM are combined using a fairly simple N-best search algorithm instead of the hidden Markov model (HMM) based network. The RNNs are implemented using massively parallel processing elements (PEs) for low latency and high throughput. The weights are quantized to 6 bits to store all of them in the on-chip memory of an FPGA. The proposed algorithm is implemented on a Xilinx XC7Z045, and the system can operate much faster than real-time.

Connected through hyperedge HE-1 (induced by source paper "recurrent neural networks an embedded computing perspective"):
  with members:
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "single stream parallelization of recurrent neural networks for low power and fast inference"
    - "effective quantization approaches for recurrent neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "a survey on methods and theories of quantized neural networks"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "tensor decomposition for compressing recurrent neural network"

Connected through hyperedge HE-2 (induced by source paper "elsa a throughput optimized design of an lstm accelerator for energy constrained devices"):
  with members:
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "c lstm enabling efficient lstm using structured compression techniques on fpgas"
    - "in datacenter performance analysis of a tensor processing unit"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "single stream parallelization of generalized lstm like rnns on a gpu"
    - "unsupervised learning of video representations using lstms"
    - "parallel multi dimensional lstm with application to fast biomedical volumetric image segmentation"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-3 (induced by source paper "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"):
  with members:
    - "alternating multi bit quantization for recurrent neural networks"
    - "lstm a search space odyssey"
    - "balanced quantization an effective and efficient approach to quantized neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "the implementation of a deep recurrent neural network language model on a xilinx fpga"
    - "finn a framework for fast scalable binarized neural network inference"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "energy efficient inference accelerator for memory augmented neural networks on an fpga"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "accelerated training for massive classification via dynamic class selection"
    - "hardware automated datafow deployment of cnns"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "quantized memory augmented neural networks"
    - "finn a framework for fast scalable binarized neural network inference"
    - "an efficient approach to boosting performance of deep spiking network training"

Connected through hyperedge HE-5 (induced by source paper "the model of an anomaly detector for hilumi lhc magnets based on recurrent neural networks and adaptive quantization"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "using spatial pooler of hierarchical temporal memory to classify noisy videos with predefined complexity"
    - "neural optimizer search with reinforcement learning"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "learning to optimize"
    - "neural architecture search with reinforcement learning"
    - "gated feedback recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "balanced quantization an effective and efficient approach to quantized neural networks"):
  with members:
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "hardware oriented approximation of convolutional neural networks"
    - "identity mappings in deep residual networks"
    - "bitwise neural networks"
    - "fast compact and high quality lstm rnn based statistical parametric speech synthesizers for mobile devices"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "deep neural networks with random gaussian weights a universal classification strategy"

Connected through hyperedge HE-7 (induced by source paper "e pur an energy efficient processing unit for recurrent neural networks"):
  with members:
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "optimizing performance of recurrent neural networks on gpus"
    - "theano a python framework for fast computation of mathematical expressions"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "fixed point quantization of deep convolutional networks"
    - "learning both weights and connections for efficient neural networks"
    - "recurrent neural networks hardware implementation on fpga"

Connected through hyperedge HE-8 (induced by source paper "fixed point optimization of deep neural networks with adaptive step size retraining"):
  with members:
    - "scaling binarized neural networks on reconfigurable logic"
    - "eie efficient inference engine on compressed deep neural network"
    - "fpga based implementation of deep neural networks using on chip memory only"
    - "resiliency of deep neural networks under quantization"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fixed point quantization of deep convolutional networks"
    - "fixed point performance analysis of recurrent neural networks"

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.CL(Computation and Language)