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: "building blocks for performance oriented dsls"
  Abstract: Domain-specific languages raise the level of abstraction in software development. While it is evident that programmers can more easily reason about very high-level programs, the same holds for compilers only if the compiler has an accurate model of the application domain and the underlying target platform. Since mapping high-level, general-purpose languages to modern, heterogeneous hardware is becoming increasingly difficult, DSLs are an attractive way to capitalize on improved hardware performance, precisely by making the compiler reason on a higher level. Implementing efficient DSL compilers is a daunting task however, and support for building performance-oriented DSLs is urgently needed. To this end, we present the Delite Framework, an extensible toolkit that drastically simplifies building embedded DSLs and compiling DSL programs for execution on heterogeneous hardware. We discuss several building blocks in some detail and present experimental results for the OptiML machine-learning DSL implemented on top of Delite.

Connected through hyperedge HE-1 (induced by source paper "effects without monads non determinism back to the meta language"):
  with members:
    - "monads need not be endofunctors"

Connected through hyperedge HE-2 (induced by source paper "demystifying differentiable programming shift reset the penultimate backpropagator"):
  with members:
    - "relay a new ir for machine learning frameworks"
    - "glow graph lowering compiler techniques for neural networks"
    - "deep learning with dynamic computation graphs"
    - "on the fly operation batching in dynamic computation graphs"
    - "efficient implementation of a higher order language with built in ad"
    - "diffsharp an ad library for net languages"
    - "theano a python framework for fast computation of mathematical expressions"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-3 (induced by source paper "group communication patterns for high performance computing in scala"):
  with members:
    - "foopar a functional object oriented parallel framework in scala"
    - "a performance comparison of cuda and opencl"

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)