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 "modeling of languages for tensor manipulation"):
  Abstract: Numerical applications and, more recently, machine learning applications rely on high-dimensional data that is typically organized into multi-dimensional tensors. Many existing frameworks, libraries, and domain-specific languages support the development of efficient code for manipulating tensors and tensor expressions. However, such frameworks and languages that are used in practice often lack formal specifications. The present report formally defines a model language for expressing tensor operations. The model language is simple and yet general enough so that it captures the fundamental tensor operations common to most existing languages and frameworks. It is shown that the given formal semantics are sensible, in the sense that well-typed programs in the model language execute correctly, without error. Moreover, an alternative implementation of the model language is formally defined. The alternative implementation introduces padding into the storage of tensors, which may benefit performance on modern hardware platforms. Based on their formal definitions, the original implementation of the model language and the implementation with padding are proven equivalent. Finally, some possible extensions of the presented model language for tensor manipulation are discussed.

Includes members:
  Member "typesafe abstractions for tensor operations short paper":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ariadne analysis for machine learning programs")
  Member "design of a high performance gemm like tensor tensor multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "yet another tensor toolbox for discontinuous galerkin methods and other applications")
  Member "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.PL(Programming Languages)