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: "tiramisu a polyhedral compiler for expressing fast and portable code"
  Abstract: This paper introduces Tiramisu, a polyhedral framework designed to generate high performance code for multiple platforms including multicores, GPUs, and distributed machines. Tiramisu introduces a scheduling language with novel extensions to explicitly manage the complexities that arise when targeting these systems. The framework is designed for the areas of image processing, stencils, linear algebra and deep learning. Tiramisu has two main features: it relies on a flexible representation based on the polyhedral model and it has a rich scheduling language allowing fine-grained control of optimizations. Tiramisu uses a four-level intermediate representation that allows full separation between the algorithms, loop transformations, data layouts, and communication. This separation simplifies targeting multiple hardware architectures with the same algorithm. We evaluate Tiramisu by writing a set of image processing, deep learning, and linear algebra benchmarks and compare them with state-of-the-art compilers and hand-tuned libraries. We show that Tiramisu matches or outperforms existing compilers and libraries on different hardware architectures, including multicore CPUs, GPUs, and distributed machines.

Connected through hyperedge HE-1 (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"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "learning to optimize tensor programs"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "in datacenter performance analysis of a tensor processing unit"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "compiling neural networks for a computational memory accelerator"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "stripe tensor compilation via the nested polyhedral model"
    - "a fast analytical model of fully associative caches"
    - "automated derivation of parametric data movement lower bounds for affine programs"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "glow graph lowering compiler techniques for neural networks"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"
    - "a hardware software blueprint for flexible deep learning specialization"

Connected through hyperedge HE-3 (induced by source paper "anyhls high level synthesis with partial evaluation"):
  with members:
    - "module per object a human driven methodology for c based high level synthesis design"
    - "transformations of high level synthesis codes for high performance computing"
    - "automated accelerator generation and optimization with composable parallel and pipeline architecture"
    - "programming heterogeneous systems from an image processing dsl"
    - "c templates as partial evaluation"

Connected through hyperedge HE-4 (induced by source paper "optimizing ordered graph algorithms with graphit"):
  with members:
    - "start late or finish early a distributed graph processing system with redundancy reduction"
    - "distributionally linearizable data structures"
    - "gunrock gpu graph analytics"
    - "the gap benchmark suite"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-5 (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"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"

Connected through hyperedge HE-6 (induced by source paper "mlir a compiler infrastructure for the end of moore s law"):
  with members:
    - "stripe tensor compilation via the nested polyhedral model"
    - "glow graph lowering compiler techniques for neural networks"
    - "tvm an automated end to end optimizing compiler for deep learning"

Connected through hyperedge HE-7 (induced by source paper "a language for describing optimization strategies"):
  with members:
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "strategy preserving compilation for parallel functional code"
    - "porgy strategy driven interactive transformation of graphs"

Connected through hyperedge HE-8 (induced by source paper "differentiable visual computing"):
  with members:
    - "on the convergence of adam and beyond"
    - "elimination of all bad local minima in deep learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "stochastic gradient descent with biased but consistent gradient estimators"
    - "line drawings from 3d models"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "tangent automatic differentiation using source code transformation in python"
    - "approximate program smoothing using mean variance statistics with application to procedural shader bandlimiting"

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)