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: "design of synchronous section carry based carry lookahead adders with improved figure of merit"
  Abstract: The section-carry based carry lookahead adder (SCBCLA) architecture was proposed as an efficient alternative to the conventional carry lookahead adder (CCLA) architecture for the physical implementation of computer arithmetic. In previous related works, self-timed SCBCLA architectures and synchronous SCBCLA architectures were realized using standard cells and FPGAs. In this work, we deal with improved realizations of synchronous SCBCLA architectures designed in a semi-custom fashion using standard cells. The improvement is quantified in terms of a figure of merit (FOM), where the FOM is defined as the inverse product of power, delay and area. Since power, delay and area of digital designs are desirable to be minimized, the FOM is desirable to be maximized. Starting from an efficient conventional carry lookahead generator, we show how an optimized section-carry based carry lookahead generator is realized. In comparison with our recent work dealing with standard cells based implementation of SCBCLAs to perform 32-bit addition of two binary operands, we show in this work that with improved section-carry based carry lookahead generators, the resulting SCBCLAs exhibit significant improvements in FOM. Compared to the earlier optimized hybrid SCBCLA, the proposed optimized hybrid SCBCLA improves the FOM by 88.3%. Even the optimized hybrid CCLA features improvement in FOM by 77.3% over the earlier optimized hybrid CCLA. However, the proposed optimized hybrid SCBCLA is still the winner and has a better FOM than the currently optimized hybrid CCLA by 15.3%. All the CCLAs and SCBCLAs are implemented to realize 32-bit dual-operand binary addition using a 32/28nm CMOS process.

Connected through hyperedge HE-1 (induced by source paper "performance comparison of some synchronous adders"):
  with members:
    - "power delay and area comparisons of majority voters relevant to tmr architectures"
    - "a fault tolerance improved majority voter for tmr system architectures"

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)