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: "high performance ultra low precision convolutions on mobile devices"
  Abstract: Many applications of mobile deep learning, especially real-time computer vision workloads, are constrained by computation power. This is particularly true for workloads running on older consumer phones, where a typical device might be powered by a single- or dual-core ARMv7 CPU. We provide an open-source implementation and a comprehensive analysis of (to our knowledge) the state of the art ultra-low-precision (<4 bit precision) implementation of the core primitives required for modern deep learning workloads on ARMv7 devices, and demonstrate speedups of 4x-20x over our additional state-of-the-art float32 and int8 baselines.

Connected through hyperedge HE-1 (induced by source paper "propagating asymptotic estimated gradients for low bitwidth quantized neural networks"):
  with members:
    - "precision highway for ultra low precision quantization"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "relaxed quantization for discretized neural networks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "pact parameterized clipping activation for quantized neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-2 (induced by source paper "the two pass softmax algorithm"):
  with members:
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "building a web scale dependency parsed corpus from commoncrawl"
    - "efficient softmax approximation for gpus"
    - "eie efficient inference engine on compressed deep neural network"
    - "faster cnns with direct sparse convolutions and guided pruning"
    - "exploring the limits of language modeling"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "extreme language model compression with optimal subwords and shared projections"):
  with members:
    - "q bert hessian based ultra low precision quantization of bert"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "patient knowledge distillation for bert model compression"
    - "cross lingual language model pretraining"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "precision highway for ultra low precision quantization"):
  with members:
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "pact parameterized clipping activation for quantized neural networks"
    - "wrpn wide reduced precision networks"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "visualizing the loss landscape of neural nets"
    - "towards effective low bitwidth convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "automating generation of low precision deep learning operators"):
  with members:
    - "tvm end to end optimization stack for deep learning"
    - "learning to optimize tensor programs"
    - "heterogeneous bitwidth binarization in convolutional neural networks"
    - "faster population counts using avx2 instructions"
    - "deep learning with low precision by half wave gaussian quantization"
    - "finn a framework for fast scalable binarized neural network inference"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-6 (induced by source paper "tvm end to end optimization stack for deep learning"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dlvm a modern compiler infrastructure for deep learning systems"
    - "finn a framework for fast scalable binarized neural network inference"
    - "xgboost a scalable tree boosting system"
    - "identity mappings in deep residual networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "rethinking floating point for deep learning"):
  with members:
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "deep learning with low precision by half wave gaussian quantization"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "ternary weight networks"
    - "convolutional neural networks using logarithmic data representation"
    - "fixed point quantization of deep convolutional networks"
    - "deep residual learning for image recognition"

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)