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 case for superconducting accelerators"
  Abstract: As the scaling of conventional CMOS-based technologies slows down, there is growing interest in alternative technologies that can improve performance or energy-efficiency. Superconducting circuits based on Josephson Junction (JJ) is an emerging technology that can provide devices which can be switched with pico-second latencies and consuming two orders of magnitude lower switching energy compared to CMOS. While JJ-based circuits can provide high operating frequency and energy-efficiency, this technology faces three critical challenges: limited device density and lack of area-efficient technology for memory structures, reduced gate fanout compared to CMOS, and new failure modes of Flux-Traps that occurs due to the operating environment. #R##N#The lack of dense memory technology restricts the use of superconducting technology in the near term to application domains that have high compute intensity but require negligible amount of memory. In this paper, we study the use of superconducting technology to build an accelerator for SHA-256 engines commonly used in Bitcoin mining applications. We show that merely porting existing CMOS-based accelerator to superconducting technology provides 10.6X improvement in energy efficiency. Redesigning the accelerator to suit the unique constraints of superconducting technology (such as low fanout) improves the energy efficiency to 12.2X. We also investigate solutions to make the accelerator tolerant of new fault modes and show how this fault-tolerant design can be leveraged to reduce the operating current, thereby increasing the overall energy-efficiency to 46X compared to CMOS. Our paper also develops a workflow for evaluating area, performance, and power for accelerators built in superconducting technology, and this workflow can help other researchers explore designs using this technology.

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)