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: "personalized speech recognition on mobile devices"
  Abstract: We describe a large vocabulary speech recognition system that is accurate, has low latency, and yet has a small enough memory and computational footprint to run faster than real-time on a Nexus 5 Android smartphone. We employ a quantized Long Short-Term Memory (LSTM) acoustic model trained with connectionist temporal classification (CTC) to directly predict phoneme targets, and further reduce its memory footprint using an SVD-based compression scheme. Additionally, we minimize our memory footprint by using a single language model for both dictation and voice command domains, constructed using Bayesian interpolation. Finally, in order to properly handle device-specific information, such as proper names and other context-dependent information, we inject vocabulary items into the decoder graph and bias the language model on-the-fly. Our system achieves 13.5% word error rate on an open-ended dictation task, running with a median speed that is seven times faster than real-time.

Connected through hyperedge HE-1 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks"):
  with members:
    - "an optimized recurrent unit for ultra low power keyword spotting"
    - "streaming end to end speech recognition for mobile devices"
    - "donut ctc based query by example keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "automatic grammar augmentation for robust voice command recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "spoken language understanding on the edge"

Connected through hyperedge HE-2 (induced by source paper "optimizing speech recognition for the edge"):
  with members:
    - "stabilizing the lottery ticket hypothesis"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "streaming end to end speech recognition for mobile devices"
    - "toward domain invariant speech recognition via large scale training"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "the unreasonable effectiveness of the forget gate"
    - "snip single shot network pruning based on connection sensitivity"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-3 (induced by source paper "deep spiking neural networks for large vocabulary automatic speech recognition"):
  with members:
    - "a tandem learning rule for efficient and rapid inference on deep spiking neural networks"
    - "an efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks"
    - "deep learning in spiking neural networks"
    - "surrogate gradient learning in spiking neural networks"
    - "comprehensive snn compression using admm optimization and activity regularization"
    - "espnet end to end speech processing toolkit"
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "going deeper in spiking neural networks vgg and residual architectures"

Connected through hyperedge HE-4 (induced by source paper "federated user representation learning"):
  with members:
    - "federated collaborative filtering for privacy preserving personalized recommendation system"
    - "towards federated learning at scale system design"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "fair resource allocation in federated learning"
    - "pytorch biggraph a large scale graph embedding system"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"

Connected through hyperedge HE-5 (induced by source paper "gradient scheduling with global momentum for non iid data distributed asynchronous training"):
  with members:
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"
    - "federated learning with non iid data"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "hello edge keyword spotting on microcontrollers"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "asynchronous stochastic gradient descent with delay compensation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-6 (induced by source paper "two pass end to end speech recognition"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "streaming end to end speech recognition for mobile devices"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"
    - "minimum word error rate training for attention based sequence to sequence models"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "joint ctc attention based end to end speech recognition using multi task learning"

Connected through hyperedge HE-7 (induced by source paper "towards characterizing and limiting information exposure in dnn layers"):
  with members:
    - "exploiting unintended feature leakage in collaborative learning"
    - "chiron privacy preserving machine learning as a service"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "securing input data of deep learning inference systems via partitioned enclave execution"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-8 (induced by source paper "gpu accelerated viterbi exact lattice decoder for batched online and offline speech recognition"):
  with members:
    - "a gpu based wfst decoder with exact lattice generation"
    - "streaming end to end speech recognition for mobile devices"
    - "decoding with finite state transducers on gpus"

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)