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: "on the total power capacity of regular ldpc codes with iterative message passing decoders"
  Abstract: Motivated by recently derived fundamental limits on total (transmit + decoding) power for coded communication with VLSI decoders, this paper investigates the scaling behavior of the minimum total power needed to communicate over AWGN channels as the target bit-error-probability tends to zero. We focus on regular-LDPC codes and iterative message-passing decoders. We analyze scaling behavior under two VLSI complexity models of decoding. One model abstracts power consumed in processing elements (node model), and another abstracts power consumed in wires which connect the processing elements (wire model). We prove that a coding strategy using regular-LDPC codes with Gallager-B decoding achieves order-optimal scaling of total power under the node model. However, we also prove that regular-LDPC codes and iterative message-passing decoders cannot meet existing fundamental limits on total power under the wire model. Furthermore, if the transmit energy-per-bit is bounded, total power grows at a rate that is  worse  than uncoded transmission. Complementing our theoretical results, we develop detailed physical models of decoding implementations using post-layout circuit simulations. Our theoretical and numerical results show that approaching fundamental limits on total power requires increasing the complexity of both the code design and the corresponding decoding algorithm as communication distance is increased or error-probability is lowered.

Connected through hyperedge HE-1 (induced by source paper "energy latency and reliability tradeoffs in coding circuits"):
  with members:
    - "information theory of molecular communication directions and challenges"
    - "energy harvesting transmitters that heat up throughput maximization under temperature constraints"
    - "on scaling rules for energy of vlsi polar encoders and decoders"
    - "information friction and its implications on minimum energy required for communication"
    - "on the energy complexity of ldpc decoder circuits"
    - "energy efficient decoders for compressive sensing fundamental limits and implementations"
    - "modeling and energy optimization of ldpc decoder circuits with timing violations"
    - "computing linear transformations with unreliable components"

Connected through hyperedge HE-2 (induced by source paper "modeling and energy optimization of ldpc decoder circuits with timing violations"):
  with members:
    - "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"
    - "state dependent statistical timing model for voltage scaled circuits"
    - "energy consumption of vlsi decoders"
    - "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.IT(Information Theory)