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 "an overview of in memory processing with emerging non volatile memory for data intensive applications"):
  Abstract: The conventional von Neumann architecture has been revealed as a major performance and energy bottleneck for rising data-intensive applications. The decade-old idea of leveraging in-memory processing to eliminate substantial data movements has returned and led extensive research activities. The effectiveness of in-memory processing heavily relies on memory scalability, which cannot be satisfied by traditional memory technologies. Emerging non-volatile memories (eNVMs) that pose appealing qualities such as excellent scaling and low energy consumption, on the other hand, have been heavily investigated and explored for realizing in-memory processing architecture. In this paper, we summarize the recent research progress in eNVM-based in-memory processing from various aspects, including the adopted memory technologies, locations of the in-memory processing in the system, supported arithmetics, as well as applied applications.

Includes members:
  Member "red a reram based deconvolution accelerator":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hypar towards hybrid parallelism for deep learning accelerator array")
  Member "mixed precision in memory computing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mixed precision deep learning based on computational memory")
  Member "x sram enabling in memory boolean computations in cmos static random access memories":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "eva cim a system level performance and energy evaluation framework for computing in memory architectures")
  Member "temporal correlation detection using computational phase change memory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mixed precision deep learning based on computational memory")
  Member "computing in memory with spin transfer torque magnetic ram":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a new mram based process in memory accelerator for efficient neural network training with floating point precision")
  Member "graphr accelerating graph processing using reram":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on graph processing accelerators challenges and opportunities")
  Member "xnor net imagenet classification using binary convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)