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: "ternary neural networks for resource efficient ai applications"
  Abstract: The computation and storage requirements for Deep Neural Networks (DNNs) are usually high. This issue limits their deployability on ubiquitous computing devices such as smart phones, wearables and autonomous drones. In this paper, we propose ternary neural networks (TNNs) in order to make deep learning more resource-efficient. We train these TNNs using a teacher-student approach based on a novel, layer-wise greedy methodology. Thanks to our two-stage training procedure, the teacher network is still able to use state-of-the-art methods such as dropout and batch normalization to increase accuracy and reduce training time. Using only ternary weights and activations, the student ternary network learns to mimic the behavior of its teacher network without using any multiplication. Unlike its -1,1 binary counterparts, a ternary neural network inherently prunes the smaller weights by setting them to zero during training. This makes them sparser and thus more energy-efficient. We design a purpose-built hardware architecture for TNNs and implement it on FPGA and ASIC. We evaluate TNNs on several benchmark datasets and demonstrate up to 3.1x better energy efficiency with respect to the state of the art while also improving accuracy.

Connected through hyperedge HE-1 (induced by source paper "pre defined sparsity for low complexity convolutional neural networks"):
  with members:
    - "pre defined sparse neural networks with hardware acceleration"
    - "scienet deep learning with spike assisted contextual information extraction"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-2 (induced by source paper "qutibench benchmarking neural networks on heterogeneous hardware"):
  with members:
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "binareye an always on energy accuracy scalable binary cnn processor with all memory on chip in 28nm cmos"
    - "training and inference with integers in deep neural networks"
    - "tvm end to end optimization stack for deep learning"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "bismo a scalable bit serial matrix multiplication overlay for reconfigurable computing"
    - "tbd benchmarking and analyzing deep neural network training"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-3 (induced by source paper "ternary mobilenets via per layer hybrid filter banks"):
  with members:
    - "ternary hybrid neural tree networks for highly constrained iot applications"
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "searching for mobilenetv3"
    - "run time efficient rnn compression for inference on edge devices"
    - "pushing the limits of rnn compression"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-4 (induced by source paper "psdnet and dpdnet efficient channel expansion depthwise pointwise depthwise inverted bottleneck block"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "cinic 10 is not imagenet or cifar 10"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "igcv 2 interleaved structured sparse convolutional neural networks"
    - "recent advances in convolutional neural network acceleration"
    - "rethinking the value of network pruning"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "ternary hybrid neural tree networks for highly constrained iot applications"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "hello edge keyword spotting on microcontrollers"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep learning with low precision by half wave gaussian quantization"
    - "morphnet fast simple resource constrained structure learning of deep networks"

Connected through hyperedge HE-6 (induced by source paper "a neural network prefetcher for arbitrary memory access patterns"):
  with members:
    - "learning memory access patterns"
    - "in datacenter performance analysis of a tensor processing unit"
    - "lstm a search space odyssey"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "bitwise neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "neural random access machines"

Connected through hyperedge HE-7 (induced by source paper "density encoding enables resource efficient randomly connected neural networks"):
  with members:
    - "a theory of sequence indexing and working memory in recurrent neural networks"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "the high dimensional geometry of binary neural networks"
    - "bitwise neural networks"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "representing objects relations and sequences"
    - "provably bounded optimal agents"

Connected through hyperedge HE-8 (induced by source paper "neural networks weights quantization target none retraining ternary tnt"):
  with members:
    - "gxnor net training deep neural networks with ternary weights and activations without full precision memory under a unified discretization framework"
    - "ternary neural networks with fine grained quantization"
    - "ternary weight networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "very deep convolutional networks for large scale 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)