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 "compiling neural networks for a computational memory accelerator"):
  Abstract: Computational memory (CM) is a promising approach for accelerating inference on neural networks (NN) by using enhanced memories that, in addition to storing data, allow computations on them. One of the main challenges of this approach is defining a hardware/software interface that allows a compiler to map NN models for efficient execution on the underlying CM accelerator. This is a non-trivial task because efficiency dictates that the CM accelerator is explicitly programmed as a dataflow engine where the execution of the different NN layers form a pipeline. In this paper, we present our work towards a software stack for executing ML models on such a multi-core CM accelerator. We describe an architecture for the hardware and software, and focus on the problem of implementing the appropriate control logic so that data dependencies are respected. We propose a solution to the latter that is based on polyhedral compilation.

Includes members:
  Member "puma a programmable ultra efficient memristor based accelerator for machine learning inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploiting oxide based resistive ram variability for bayesian neural network hardware design")
  Member "stripe tensor compilation via the nested polyhedral model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "a fast analytical model of fully associative caches":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "polyscientist automatic loop transformations combined with microkernels for optimization of deep learning primitives")
  Member "automated derivation of parametric data movement lower bounds for affine programs":
    also connected through other hyperedges:
      (none)
  Member "quantizing deep convolutional networks for efficient inference a whitepaper":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "glow graph lowering compiler techniques for neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "rxnn a framework for evaluating deep neural networks on resistive crossbars":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "noisy machines understanding noisy neural networks and enhancing robustness to analog hardware errors using distillation")
  Member "a hardware software blueprint for flexible deep learning specialization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the deep learning compiler a comprehensive survey")

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.DC(Distributed, Parallel, and Cluster Computing)