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: "state dependent statistical timing model for voltage scaled circuits"
  Abstract: This paper presents a novel statistical state-dependent timing model for voltage over scaled (VoS) logic circuits that accurately and rapidly finds the timing distribution of output bits. Using this model erroneous VoS circuits can be represented as error-free circuits combined with an error-injector. A case study of a two point DFT unit employing the proposed model is presented and compared to HSPICE circuit simulation. Results show an accurate match, with significant speedup gains.

Connected through hyperedge HE-1 (induced by source paper "modeling and energy optimization of ldpc decoder circuits with timing violations"):
  with members:
    - "on the total power capacity of regular ldpc codes with iterative message passing decoders"
    - "on the energy complexity of ldpc decoder circuits"
    - "analysis and design of finite alphabet iterative decoders robust to faulty hardware"
    - "density evolution for min sum decoding of ldpc codes under unreliable message storage"
    - "energy consumption of vlsi decoders"
    - "performance of ldpc codes under faulty iterative decoding"

Connected through hyperedge HE-2 (induced by source paper "majority logic decoding under data dependent logic gate failures"):
  with members:
    - "modeling and energy optimization of ldpc decoder circuits with timing violations"
    - "density evolution and functional threshold for the noisy min sum decoder"
    - "density evolution for min sum decoding of ldpc codes under unreliable message storage"
    - "on trapping sets and guaranteed error correction capability of ldpc codes and gldpc codes"
    - "performance of ldpc codes under faulty iterative decoding"

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)