human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "on the energy complexity of ldpc decoder circuits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "energy latency and reliability tradeoffs in coding circuits")
  Member "energy consumption of vlsi decoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressive sensing based user clustering for downlink noma systems with decoding power")
  Member "towards a communication theoretic understanding of system level power consumption":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy efficient beam coordination strategies with rate dependent processing power")
  Member "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reed muller codes theory and algorithms")
  Member "two bit message passing decoders for ldpc codes over the binary symmetric channel":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "coding for optical communications can we approach the shannon limit with low complexity")
  Member "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient qp admm decoder for binary ldpc codes and its performance analysis")

Question: Please tell me which class the source paper 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)