human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a mechanism for balancing accuracy and scope in cross machine black box gpu performance modeling"):
  Abstract: The ability to model, analyze, and predict execution time of computations is an important building block supporting numerous efforts, such as load balancing, performance optimization, and automated performance tuning for high performance, parallel applications. In today's increasingly heterogeneous computing environment, this task must be accomplished efficiently across multiple architectures, including massively parallel coprocessors like GPUs. To address this challenge, we present an approach for constructing customizable, cross-machine performance models for GPU kernels, including a mechanism to automatically and symbolically gather performance-relevant kernel operation counts, a tool for formulating mathematical models using these counts, and a customizable parameterized collection of benchmark kernels used to fit models to GPUs in a black-box fashion. Our approach empowers a user to manage trade-offs between model accuracy, evaluation speed, and generalizability. A user can define a model and customize the fitting process, making it as simple or complex as desired, and as application-targeted or general-purpose as desired. As application examples of our approach, we demonstrate both linear and nonlinear models; each example models execution times for multiple variants of a particular computation: two matrix multiplication variants, four Discontinuous Galerkin (DG) differentiation operation variants, and two 2-D first-order finite difference computation variants. For each variant, we present accuracy results on GPUs from multiple vendors and hardware generations. We view this customizable approach as a response to a central question in GPU performance modeling: how can we model GPU performance in a cost-explanatory fashion while maintaining accuracy, evaluation speed, portability, and ease of use, an attribute we believe precludes manual collection of kernel or hardware statistics.

Includes members:
  Member "learning to optimize tensor programs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "opencl performance prediction using architecture independent features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "characterizing optimizations to memory access patterns using architecture independent program features")
  Member "kerncraft a tool for analytic performance modeling of loop kernels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "collecting and presenting reproducible intranode stencil performance inspect")
  Member "loo py from fortran to performance via transformation and substitution rules":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "array program transformation with loo py by example high order finite elements")
  Member "loo py transformation based code generation for gpus and cpus":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a study of vectorization for matrix free finite element methods")
  Member "introducing a performance model for bandwidth limited loop kernels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analytic performance modeling and analysis of detailed neuron simulations")

Question: Please tell me which class the source paper 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.PF(Performance)