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: "a row parallel 8 times 8 2 d dct architecture using algebraic integer based exact computation"
  Abstract: An algebraic integer (AI)-based time-multiplexed row-parallel architecture and two final reconstruction step (FRS) algorithms are proposed for the implementation of bivariate AI encoded 2-D discrete cosine transform (DCT). The architecture directly realizes an error-free 2-D DCT without using FRSs between row-column transforms, leading to an 8 × 8 2-D DCT that is entirely free of quantization errors in AI basis. As a result, the user-selectable accuracy for each of the coefficients in the FRS facilitates each of the 64 coefficients to have its precision set independently of others, avoiding the leakage of quantization noise between channels as is the case for published DCT designs. The proposed FRS uses two approaches based on: 1) optimized Dempster-Macleod multipliers, and 2) expansion factor scaling. This architecture enables low-noise high-dynamic range applications in digital video processing that requires full control of the finite-precision computation of the 2-D DCT. The proposed architectures and FRS techniques are experimentally verified and validated using hardware implementations that are physically realized and verified on field-programmable gate array (FPGA) chip. Six designs, for 4-bit and 8-bit input word sizes, using the two proposed FRS schemes, have been designed, simulated, physically implemented, and measured. The maximum clock rate and block rate achieved among 8-bit input designs are 307.787 MHz and 38.47 MHz, respectively, implying a pixel rate of 8 × 307.787≈2.462 GHz if eventually embedded in a real- time video-processing system. The equivalent frame rate is about 1187.35Hz for the image size of 1920 × 1080. All implementations are functional on a Xilinx Virtex-6 XC6VLX240T FPGA device.

Connected through hyperedge HE-1 (induced by source paper "improved 8 point approximate dct for image and video compression requiring only 14 additions"):
  with members:
    - "dct like transform for image compression requires 14 additions only"
    - "a digital hardware fast algorithm and fpga based prototype for a novel 16 point approximate dct for image compression applications"
    - "a dct approximation for image compression"

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)