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: "tvm an automated end to end optimizing compiler for deep learning"
  Abstract: There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narrow range of server-class GPUs. Deploying workloads to new platforms -- such as mobile phones, embedded devices, and accelerators (e.g., FPGAs, ASICs) -- requires significant manual effort. We propose TVM, a compiler that exposes graph-level and operator-level optimizations to provide performance portability to deep learning workloads across diverse hardware back-ends. TVM solves optimization challenges specific to deep learning, such as high-level operator fusion, mapping to arbitrary hardware primitives, and memory latency hiding. It also automates optimization of low-level programs to hardware characteristics by employing a novel, learning-based cost modeling method for rapid exploration of code optimizations. Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs. We also demonstrate TVM's ability to target new accelerator back-ends, such as the FPGA-based generic deep learning accelerator. The system is open sourced and in production use inside several major companies.

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 "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning"):
  with members:
    - "deep learning on mobile devices a review"
    - "progressive dnn compression a key to achieve ultra high weight pruning and quantization rates using admm"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deepcache principled cache for mobile deep vision"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "amc automl for model compression and acceleration on mobile devices"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

Connected through hyperedge HE-3 (induced by source paper "cocopie making mobile ai sweet as pie compression compilation co design goes a long way"):
  with members:
    - "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "wide activation for efficient and accurate image super resolution"
    - "multi style generative network for real time transfer"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "exploring the regularity of sparse structure in convolutional neural networks"

Connected through hyperedge HE-4 (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-5 (induced by source paper "an image enhancing pattern based sparsity for real time inference on mobile devices"):
  with members:
    - "autoslim towards one shot architecture search for channel numbers"
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "deepcache principled cache for mobile deep vision"
    - "amc automl for model compression and acceleration on mobile devices"
    - "discrimination aware channel pruning for deep neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "rethinking the value of network pruning"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-6 (induced by source paper "blk rew a unified block based dnn pruning framework using reweighted regularization method"):
  with members:
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "amc automl for model compression and acceleration on mobile devices"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "e rnn design optimization for efficient recurrent neural networks in fpgas"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-7 (induced by source paper "marvel a decoupled model driven approach for efficiently mapping convolutions on spatial dnn accelerators"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "in datacenter performance analysis of a tensor processing unit"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a systematic approach to blocking convolutional neural networks"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "polyscientist automatic loop transformations combined with microkernels for optimization of deep learning primitives"):
  with members:
    - "a fast analytical model of fully associative caches"
    - "high performance deep learning via a single building block"
    - "learning to optimize tensor programs"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"
    - "in datacenter performance analysis of a tensor processing unit"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mask r cnn"

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)