human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "ara a 1 ghz scalable and energy efficient risc v vector processor with multi precision floating point support in 22 nm fd soi"
  Abstract: In this paper, we present Ara, a 64-bit vector processor based on the version 0.5 draft of RISC-V's vector extension, implemented in GlobalFoundries 22FDX FD-SOI technology. Ara's microarchitecture is scalable, as it is composed of a set of identical lanes, each containing part of the processor's vector register file and functional units. It achieves up to 97% FPU utilization when running a 256 x 256 double precision matrix multiplication on sixteen lanes. Ara runs at 1.2 GHz in the typical corner (TT/0.80 V/25 oC), achieving a performance up to 34 DP-GFLOPS. In terms of energy efficiency, Ara achieves up to 67 DP-GFLOPS/W under the same conditions, which is 56% higher than similar vector processors found in literature. An analysis on several vectorizable linear algebra computation kernels for a range of different matrix and vector sizes gives insight into performance limitations and bottlenecks for vector processors and outlines directions to maintain high energy efficiency even for small matrix sizes where the vector architecture achieves suboptimal utilization of the available FPUs.

Connected through hyperedge HE-1 (induced by source paper "vortex opencl compatible risc v gpgpu"):
  with members:
    - "grvi phalanx a massively parallel risc v fpga accelerator accelerator"

Connected through hyperedge HE-2 (induced by source paper "stream semantic registers a lightweight risc v isa extension achieving full compute utilization in single issue cores"):
  with members:
    - "the cost of application class processing energy and performance analysis of a linux ready 1 7 ghz 64 bit risc v core in 22 nm fdsoi technology"
    - "xnor neural engine a hardware accelerator ip for 21 6 fj op binary neural network inference"
    - "a scalable near memory architecture for training deep neural networks on large in memory datasets"

Question: Please tell me which class the center node 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)