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 "low density majority codes and the problem of graceful degradation"):
  Abstract: We study a problem of constructing codes that transform a channel with high bit error rate (BER) into one with low BER (at the expense of rate). Our focus is on obtaining codes with smooth ("graceful'') input-output BER curves (as opposed to threshold-like curves typical for long error-correcting codes). #R##N#This paper restricts attention to binary erasure channels (BEC) and contains three contributions. First, we introduce the notion of Low Density Majority Codes (LDMCs). These codes are non-linear sparse-graph codes, which output majority function evaluated on randomly chosen small subsets of the data bits. This is similar to Low Density Generator Matrix codes (LDGMs), except that the XOR function is replaced with the majority. We show that even with a few iterations of belief propagation (BP) the attained input-output curves provably improve upon performance of any linear systematic code. The effect of non-linearity bootstraping the initial iterations of BP, suggests that LDMCs should improve performance in various applications, where LDGMs have been used traditionally. #R##N#Second, we establish several \textit{two-point converse bounds} that lower bound the BER achievable at one erasure probability as a function of BER achieved at another one. The novel nature of our bounds is that they are specific to subclasses of codes (linear systematic and non-linear systematic) and outperform similar bounds implied by the area theorem for the EXIT function.

Includes members:
  Member "a lower bound on the expected distortion of joint source channel coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on low density majority codes")
  Member "low complexity concatenated ldpc staircase codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "binary message passing decoding of product like codes")
  Member "comparison of channels criteria for domination by a symmetric channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a novel approach to multivariate redundancy and synergy")
  Member "deep joint source channel coding for wireless image transmission":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep joint transmission recognition for power constrained iot devices")
  Member "strong data processing inequalities for channels and bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "a source channel separation theorem with application to the source broadcast problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a lower bound on the expected distortion of joint source channel coding")
  Member "protograph based raptor like ldpc codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ultrareliable and low latency communication techniques for tactile internet services")
  Member "intermediate performance of rateless codes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on low density majority codes")

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)