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: "tensor comprehensions framework agnostic high performance machine learning abstractions"
  Abstract: Deep learning models with convolutional and recurrent networks are now ubiquitous and analyze massive amounts of audio, image, video, text and graph data, with applications in automatic translation, speech-to-text, scene understanding, ranking user preferences, ad placement, etc. Competing frameworks for building these networks such as TensorFlow, Chainer, CNTK, Torch/PyTorch, Caffe1/2, MXNet and Theano, explore different tradeoffs between usability and expressiveness, research or production orientation and supported hardware. They operate on a DAG of computational operators, wrapping high-performance libraries such as CUDNN (for NVIDIA GPUs) or NNPACK (for various CPUs), and automate memory allocation, synchronization, distribution. Custom operators are needed where the computation does not fit existing high-performance library calls, usually at a high engineering cost. This is frequently required when new operators are invented by researchers: such operators suffer a severe performance penalty, which limits the pace of innovation. Furthermore, even if there is an existing runtime call these frameworks can use, it often doesn't offer optimal performance for a user's particular network architecture and dataset, missing optimizations between operators as well as optimizations that can be done knowing the size and shape of data. Our contributions include (1) a language close to the mathematics of deep learning called Tensor Comprehensions offering both imperative and declarative styles, (2) a polyhedral Just-In-Time compiler to convert a mathematical description of a deep learning DAG into a CUDA kernel with delegated memory management and synchronization, also providing optimizations such as operator fusion and specialization for specific sizes, (3) a compilation cache populated by an autotuner. [Abstract cutoff]

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"
    - "tvm an automated end to end optimizing compiler for deep learning"

Connected through hyperedge HE-3 (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-4 (induced by source paper "neural kernels without tangents"):
  with members:
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "do imagenet classifiers generalize to imagenet"
    - "on exact computation with an infinitely wide neural net"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "a kernel theory of modern data augmentation"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "a unified iteration space transformation framework for sparse and dense tensor algebra"):
  with members:
    - "load balanced sparse mttkrp on gpus"
    - "format abstraction for sparse tensor algebracompilers"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "tensorflow a system for large scale machine learning"
    - "csr5 an efficient storage format for cross platform sparse matrix vector multiplication"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "fireiron a scheduling language for high performance linear algebra on gpus"):
  with members:
    - "a proposal for loop transformation pragmas"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning and adaptive sampling for optimized dnn compilation"):
  with members:
    - "active learning for regression using greedy sampling"
    - "meta gradient reinforcement learning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "learning to optimize tensor programs"
    - "efficient neural architecture search via parameter sharing"
    - "deep reinforcement learning that matters"
    - "deep reinforcement learning for resource allocation in v2v communications"

Connected through hyperedge HE-8 (induced by source paper "high performance deep learning via a single building block"):
  with members:
    - "stripe tensor compilation via the nested polyhedral model"
    - "large batch training for lstm and beyond"
    - "nvidia tensor core programmability performance precision"
    - "high accuracy low precision training"
    - "glow graph lowering compiler techniques for neural networks"
    - "anatomy of high performance deep learning convolutions on simd architectures"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"

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.PL(Programming Languages)