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: "dlvm a modern compiler infrastructure for deep learning systems"
  Abstract: Deep learning software demands reliability and performance. However, many of the existing deep learning frameworks are software libraries that act as an unsafe DSL in Python and a computation graph interpreter. We present DLVM, a design and implementation of a compiler infrastructure with a linear algebra intermediate representation, algorithmic differentiation by adjoint code generation, domain-specific optimizations and a code generator targeting GPU via LLVM. Designed as a modern compiler infrastructure inspired by LLVM, DLVM is more modular and more generic than existing deep learning compiler frameworks, and supports tensor DSLs with high expressivity. With our prototypical staged DSL embedded in Swift, we argue that the DLVM system enables a form of modular, safe and performant frameworks for deep learning.

Connected through hyperedge HE-1 (induced by source paper "mnn a universal and efficient inference engine"):
  with members:
    - "mlperf inference benchmark"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "fast algorithms for convolutional neural networks"

Connected through hyperedge HE-2 (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"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "towards efficient convolutional neural network for domain specific applications on fpga"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"

Connected through hyperedge HE-3 (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"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "tapirxla embedding fork join parallelism into the xla compiler in tensorflow using tapir"):
  with members:
    - "lstm benchmarks for deep learning frameworks"
    - "don t unroll adjoint differentiating ssa form programs"
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "glow graph lowering compiler techniques for neural networks"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "imagenet training in minutes"
    - "neural collaborative filtering"
    - "theano a python framework for fast computation of mathematical expressions"

Connected through hyperedge HE-5 (induced by source paper "auto vectorizing tensorflow graphs jacobians auto batching and beyond"):
  with members:
    - "spectral inference networks unifying spectral methods with deep learning"
    - "dynamic control flow in large scale machine learning"
    - "deep learning with dynamic computation graphs"
    - "neural message passing for quantum chemistry"
    - "adaptive computation time for recurrent neural networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "stripe tensor compilation via the nested polyhedral model"):
  with members:
    - "relay a new ir for machine learning frameworks"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"
    - "optimizing cnn model inference on cpus"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-7 (induced by source paper "tensorflow eager a multi stage python embedded dsl for machine learning"):
  with members:
    - "don t unroll adjoint differentiating ssa form programs"
    - "deep residual learning for image recognition"
    - "automatic differentiation in machine learning a survey"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "segmental recurrent neural networks"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-8 (induced by source paper "a quick introduction to functional verification of array intensive programs"):
  with members:
    - "glow graph lowering compiler techniques for neural networks"
    - "programmatic control of a compiler for generating high performance spatial hardware"
    - "functional equivalence checking for verification of algebraic transformations on array intensive source code"

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)