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: "intel ngraph an intermediate representation compiler and executor for deep learning"
  Abstract: The Deep Learning (DL) community sees many novel topologies published each year. Achieving high performance on each new topology remains challenging, as each requires some level of manual effort. This issue is compounded by the proliferation of frameworks and hardware platforms. The current approach, which we call "direct optimization", requires deep changes within each framework to improve the training performance for each hardware backend (CPUs, GPUs, FPGAs, ASICs) and requires $\mathcal{O}(fp)$ effort; where $f$ is the number of frameworks and $p$ is the number of platforms. While optimized kernels for deep-learning primitives are provided via libraries like Intel Math Kernel Library for Deep Neural Networks (MKL-DNN), there are several compiler-inspired ways in which performance can be further optimized. Building on our experience creating neon (a fast deep learning library on GPUs), we developed Intel nGraph, a soon to be open-sourced C++ library to simplify the realization of optimized deep learning performance across frameworks and hardware platforms. Initially-supported frameworks include TensorFlow, MXNet, and Intel neon framework. Initial backends are Intel Architecture CPUs (CPU), the Intel(R) Nervana Neural Network Processor(R) (NNP), and NVIDIA GPUs. Currently supported compiler optimizations include efficient memory management and data layout abstraction. In this paper, we describe our overall architecture and its core components. In the future, we envision extending nGraph API support to a wider range of frameworks, hardware (including FPGAs and ASICs), and compiler optimizations (training versus inference optimizations, multi-node and multi-device scaling via efficient sub-graph partitioning, and HW-specific compounding of operations).

Connected through hyperedge HE-1 (induced by source paper "the deep learning compiler a comprehensive survey"):
  with members:
    - "shredder learning noise distributions to protect inference privacy"
    - "fusionstitching boosting execution efficiency of memory intensive computations for dl workloads"
    - "stripe tensor compilation via the nested polyhedral model"
    - "tensorflow eager a multi stage python embedded dsl for machine learning"
    - "privacy for rescue a new testimony why privacy is vulnerable in deep models"
    - "dissecting the graphcore ipu architecture via microbenchmarking"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "ordering chaos memory aware scheduling of irregularly wired neural networks for edge devices"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "automatic model selection for neural networks"
    - "swiftnet using graph propagation as meta knowledge to search highly representative neural architectures"
    - "discovering neural wirings"
    - "pytorch an imperative style high performance deep learning library"
    - "learned step size quantization"
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-3 (induced by source paper "miopen an open source library for deep learning primitives"):
  with members:
    - "reducing uncertainty in undersampled mri reconstruction with active acquisition"
    - "tvm end to end optimization stack for deep learning"
    - "glow graph lowering compiler techniques for neural networks"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "simple recurrent units for highly parallelizable recurrence"
    - "imagenet training in minutes"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-4 (induced by source paper "relay a high level compiler for deep learning"):
  with members:
    - "autograph imperative style coding with graph based performance"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "glow graph lowering compiler techniques for neural networks"
    - "a hardware software blueprint for flexible deep learning specialization"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "tangent automatic differentiation using source code transformation for dynamically typed array programming"

Connected through hyperedge HE-5 (induced by source paper "performance and power evaluation of ai accelerators for training deep learning models"):
  with members:
    - "automl a survey of the state of the art"
    - "the impact of gpu dvfs on the energy and performance of deep learning an empirical study"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "image classification at supercomputer scale"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "in datacenter performance analysis of a tensor processing unit"
    - "gpgpu performance estimation with core and memory frequency scaling"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "benchmarking the performance and power of ai accelerators for ai training"):
  with members:
    - "automl a survey of the state of the art"
    - "the impact of gpu dvfs on the energy and performance of deep learning an empirical study"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "image classification at supercomputer scale"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "gpgpu performance estimation with core and memory frequency scaling"
    - "attention is all you need"
    - "mixed precision training"

Connected through hyperedge HE-7 (induced by source paper "dnnvm end to end compiler leveraging heterogeneous optimizations on fpga based cnn accelerators"):
  with members:
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "tvm end to end optimization stack for deep learning"
    - "yolov3 an incremental improvement"
    - "vta an open hardware software stack for deep learning"
    - "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"
    - "towards efficient convolutional neural network for domain specific applications on fpga"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "optimizing cnn model inference on cpus"

Connected through hyperedge HE-8 (induced by source paper "swtvm exploring the automated compilation for deep learning on sunway architecture"):
  with members:
    - "glow graph lowering compiler techniques for neural networks"
    - "learning to optimize tensor programs"
    - "vta an open hardware software stack for deep learning"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "dlvm a modern compiler infrastructure for deep learning systems"
    - "end to end learning for self driving cars"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "tensorflow a system for large scale machine learning"

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.DC(Distributed, Parallel, and Cluster Computing)