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: "benchip benchmarking intelligence processors"
  Abstract: The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization (in both software and hardware). However, existing benchmarks are unsuitable for benchmarking intelligence processors due to their non-diversity and nonrepresentativeness. Also, the lack of a standard benchmarking methodology further exacerbates this problem. In this paper, we propose BENCHIP, a benchmark suite and benchmarking methodology for intelligence processors. The benchmark suite in BENCHIP consists of two sets of benchmarks: microbenchmarks and macrobenchmarks. The microbenchmarks consist of single-layer networks. They are mainly designed for bottleneck analysis and system optimization. The macrobenchmarks contain state-of-the-art industrial networks, so as to offer a realistic comparison of different platforms. We also propose a standard benchmarking methodology built upon an industrial software stack and evaluation metrics that comprehensively reflect the various characteristics of the evaluated intelligence processors. BENCHIP is utilized for evaluating various hardware platforms, including CPUs, GPUs, and accelerators. BENCHIP will be open-sourced soon.

Connected through hyperedge HE-1 (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-2 (induced by source paper "neural network model extraction attacks in edge devices by hearing architectural hints"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "tvm end to end optimization stack for deep learning"
    - "i know what you see power side channel attack on convolutional neural network accelerators"
    - "sok security and privacy in machine learning"
    - "stealing hyperparameters in machine learning"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-3 (induced by source paper "l1 norm batch normalization for efficient training of deep neural networks"):
  with members:
    - "training and inference with integers in deep neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "improved training of wasserstein gans"
    - "gxnor net training deep neural networks with ternary weights and activations without full precision memory under a unified discretization framework"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "aggregated residual transformations for deep neural networks"
    - "stacked generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "ai matrix a deep learning benchmark for alibaba data centers"):
  with members:
    - "ai matrix synthetic benchmarks for dnn"
    - "ai benchmark running deep neural networks on android smartphones"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tbd benchmarking and analyzing deep neural network training"
    - "neural collaborative filtering"
    - "dssd deconvolutional single shot detector"
    - "cascaded pyramid network for multi person pose estimation"
    - "detecting oriented text in natural images by linking segments"

Connected through hyperedge HE-5 (induced by source paper "benchmarking tpu gpu and cpu platforms for deep learning"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "ai matrix synthetic benchmarks for dnn"
    - "beyond data and model parallelism for deep neural networks"
    - "tvm end to end optimization stack for deep learning"
    - "glow graph lowering compiler techniques for neural networks"
    - "measuring the effects of data parallelism on neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

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.PF(Performance)