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 "understanding the impact of on chip communication on dnn accelerator performance"):
  Abstract: Deep Neural Networks have flourished at an unprecedented pace in recent years. They have achieved outstanding accuracy in fields such as computer vision, natural language processing, medicine or economics. Specifically, Convolutional Neural Networks (CNN) are particularly suited to object recognition or identification tasks. This, however, comes at a high computational cost, prompting the use of specialized GPU architectures or even ASICs to achieve high speeds and energy efficiency. ASIC accelerators streamline the execution of certain dataflows amenable to CNN computation that imply the constant movement of large amounts of data, thereby turning on-chip communication into a critical function within the accelerator. This paper studies the communication flows within CNN inference accelerators of edge devices, with the aim to justify current and future decisions in the design of the on-chip networks that interconnect their processing elements. Leveraging this analysis, we then qualitatively discuss the potential impact of introducing the novel paradigm of wireless on-chip network in this context.

Includes members:
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "understanding reuse performance and hardware cost of dnn dataflows a data centric approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "autodnnchip an automated dnn chip predictor and builder for both fpgas and asics")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "deeppose human pose estimation via deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "metric scale truncation robust heatmaps for 3d human pose estimation")

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.AR(Hardware Architecture)