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 "polystore accelerated polystore system for heterogeneous workloads"):
  Abstract: Modern real-time business analytic consist of heterogeneous workloads (e.g, database queries, graph processing, and machine learning). These analytic applications need programming environments that can capture all aspects of the constituent workloads (including data models they work on and movement of data across processing engines). Polystore systems suit such applications; however, these systems currently execute on CPUs and the slowdown of Moore's Law means they cannot meet the performance and efficiency requirements of modern workloads. We envision Polystore++, an architecture to accelerate existing polystore systems using hardware accelerators (e.g, FPGAs, CGRAs, and GPUs). Polystore++ systems can achieve high performance at low power by identifying and offloading components of a polystore system that are amenable to acceleration using specialized hardware. Building a Polystore++ system is challenging and introduces new research problems motivated by the use of hardware accelerators (e.g, optimizing and mapping query plans across heterogeneous computing units and exploiting hardware pipelining and parallelism to improve performance). In this paper, we discuss these challenges in detail and list possible approaches to address these problems.

Includes members:
  Member "relay a new ir for machine learning frameworks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "formal methods and software engineering for dl security safety and productivity for dl systems development")
  Member "rheemix in the data jungle a cross platform query optimizer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "building your cross platform application with rheem")
  Member "in rdbms hardware acceleration of advanced analytics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "accelerating generalized linear models with mlweaving a one size fits all system for any precision learning technical report")
  Member "practical design space exploration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "flexibo cost aware multi objective optimization of deep neural networks")
  Member "in datacenter performance analysis of a tensor processing unit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "empowering in memory relational database engines with native graph processing":
    also connected through other hyperedges:
      (none)
  Member "algorithmic performance accuracy trade off in 3d vision applications using hypermapper":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "futuremapping the computational structure of spatial ai systems")
  Member "pipegen data pipe generator for hybrid analytics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "building your cross platform application with rheem")

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)