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 "enabling the adoption of processing in memory challenges mechanisms future research directions"):
  Abstract: Poor DRAM technology scaling over the course of many years has caused DRAM-based main memory to increasingly become a larger system bottleneck. A major reason for the bottleneck is that data stored within DRAM must be moved across a pin-limited memory channel to the CPU before any computation can take place. This requires a high latency and energy overhead, and the data often cannot benefit from caching in the CPU, making it difficult to amortize the overhead. #R##N#Modern 3D-stacked DRAM architectures include a logic layer, where compute logic can be integrated underneath multiple layers of DRAM cell arrays within the same chip. Architects can take advantage of the logic layer to perform processing-in-memory (PIM), or near-data processing. In a PIM architecture, the logic layer within DRAM has access to the high internal bandwidth available within 3D-stacked DRAM (which is much greater than the bandwidth available between DRAM and the CPU). Thus, PIM architectures can effectively free up valuable memory channel bandwidth while reducing system energy consumption. #R##N#A number of important issues arise when we add compute logic to DRAM. In particular, the logic does not have low-latency access to common CPU structures that are essential for modern application execution, such as the virtual memory and cache coherence mechanisms. To ease the widespread adoption of PIM, we ideally would like to maintain traditional virtual memory abstractions and the shared memory programming model. This requires efficient mechanisms that can provide logic in DRAM with access to CPU structures without having to communicate frequently with the CPU. To this end, we propose and evaluate two general-purpose solutions that minimize unnecessary off-chip communication for PIM architectures. We show that both mechanisms improve the performance and energy consumption of many important memory-intensive applications.

Includes members:
  Member "using ecc dram to adaptively increase memory capacity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "processing data where it makes sense enabling in memory computation")
  Member "lazypim efficient support for cache coherence in processing in memory architectures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "processing data where it makes sense enabling in memory computation")
  Member "understanding and improving the latency of dram based memory systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "eden enabling energy efficient high performance deep neural network inference using approximate dram")
  Member "reducing dram latency at low cost by exploiting heterogeneity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "processing data where it makes sense enabling in memory computation")
  Member "rowhammer reliability analysis and security implications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rowhammer a retrospective")
  Member "buddy ram improving the performance and efficiency of bulk bitwise operations using dram":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mixed precision deep learning based on computational memory")
  Member "the processing using memory paradigm in dram bulk copy initialization bitwise and and or":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rowclone accelerating data movement and initialization using dram")
  Member "simple dram and virtual memory abstractions to enable highly efficient memory systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "processing data where it makes sense enabling in memory computation")

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)