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 "a workload and programming ease driven perspective of processing in memory"):
  Abstract: Many modern and emerging applications must process increasingly large volumes of data. Unfortunately, prevalent computing paradigms are not designed to efficiently handle such large-scale data: the energy and performance costs to move this data between the memory subsystem and the CPU now dominate the total costs of computation. This forces system architects and designers to fundamentally rethink how to design computers. Processing-in-memory (PIM) is a computing paradigm that avoids most data movement costs by bringing computation to the data. New opportunities in modern memory systems are enabling architectures that can perform varying degrees of processing inside the memory subsystem. However, there are many practical system-level issues that must be tackled to construct PIM architectures, including enabling workloads and programmers to easily take advantage of PIM. This article examines three key domains of work towards the practical construction and widespread adoption of PIM architectures. First, we describe our work on systematically identifying opportunities for PIM in real applications, and quantify potential gains for popular emerging applications (e.g., machine learning, data analytics, genome analysis). Second, we aim to solve several key issues on programming these applications for PIM architectures. Third, we describe challenges that remain for the widespread adoption of PIM.

Includes members:
  Member "processing data where it makes sense enabling in memory computation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rowhammer a retrospective")
  Member "in dram bulk bitwise execution engine":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "eden enabling energy efficient high performance deep neural network inference using approximate dram")
  Member "rowhammer a retrospective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "are we susceptible to rowhammer an end to end methodology for cloud providers")
  Member "accelerating bulk bit wise x n or operation in processing in dram platform":
    also connected through other hyperedges:
      (none)
  Member "shouji a fast and efficient pre alignment filter for sequence alignment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "in dram bulk bitwise execution engine")
  Member "enabling the adoption of processing in memory challenges mechanisms future research directions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "machine learning systems for highly distributed and rapidly growing data")
  Member "memristive devices for computation in memory":
    also connected through other hyperedges:
      (none)
  Member "what your dram power models are not telling you lessons from a detailed experimental study":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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.DC(Distributed, Parallel, and Cluster Computing)