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 "hardware based linear program decoding with the alternating direction method of multipliers"):
  Abstract: We present a hardware-based implementation of Linear Program (LP) decoding for binary linear codes. LP decoding frames error-correction as an optimization problem. In contrast, variants of Belief Propagation (BP) decoding frame error-correction as a problem of graphical inference. LP decoding has several advantages over BP-based methods, including convergence guarantees and better error-rate performance in high-reliability channels. The latter makes LP decoding attractive for optical transport and storage applications. However, LP decoding, when implemented with general solvers, does not scale to large blocklengths and is not suitable for a parallelized implementation in hardware. It has been recently shown that the Alternating Direction Method of Multipliers (ADMM) can be applied to decompose the LP decoding problem. The result is a message-passing algorithm with a structure very similar to BP. We present new intuition for this decoding algorithm as well as for its major computational primitive: projection onto the parity polytope. Furthermore, we present results for a fixed-point Verilog implementation of ADMM-LP decoding. This implementation targets a Field-Programmable Gate Array (FPGA) platform to evaluate error-rate performance and estimate resource usage. We show that Frame Error Rate (FER) performance well within 0.5dB of double-precision implementations is possible with 10-bit messages. Finally, we outline a number of research opportunities that should be explored en-route to the realization of an Application Specific Integrated Circuit (ASIC) implementation capable of gigabit per second throughput.

Includes members:
  Member "1 bit massive mu mimo precoding in vlsi":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spectral efficiency of one bit sigma delta massive mimo")
  Member "hardware based projection onto the parity polytope and probability simplex":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient qp admm decoder for binary ldpc codes and its performance analysis")
  Member "lp decodable multipermutation codes":
    also connected through other hyperedges:
      (none)
  Member "hierarchical and high girth qc ldpc codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi dimensional spatially coupled code design enhancing the cycle properties")
  Member "error floor approximation for ldpc codes in the awgn channel":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a combinatorial methodology for optimizing non binary graph based codes theoretical analysis and applications in data storage")
  Member "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "admm based decoder for binary linear codes aided by deep learning")
  Member "decomposition methods for large scale lp decoding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "admm based decoder for binary linear codes aided by deep learning")
  Member "adaptive methods for linear programming decoding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)