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 "exhausting error prone patterns in ldpc codes"):
  Abstract: It is proved in this work that exhaustively de- termining bad patterns in arbitrary, finite low-density par ity- check (LDPC) codes, including stopping sets for binary era- sure channels (BECs) and trapping sets (also known as near- codewords) for general memoryless symmetric channels, is an NP-complete problem, and efficient algorithms are provided for codes of practical short lengths n ≈ 500. By exploiting the sparse connectivity of LDPC codes, the stopping sets of size ≤ 13 and the trapping sets of size ≤ 11 can be efficiently exhaustively determined for the first time, and the resulting exhaustive l ist is of great importance for code analysis and finite code optimiz ation. The featured tree-based narrowing search distinguishes this algorithm from existing ones for which inexhaustive methods are employed. One important byproduct is a pair of upper bounds on the bit-error rate (BER) & frame-error rate (FER) iterative decoding performance of arbitrary codes over BECs that can be evaluated for any value of the erasure probability, including both the waterfall and the error floor regions. The tightness of these upper bounds and the exhaustion capability of the proposed algorithm are proved when combining an optimal leaf-finding module with the tree-based search. These upper bounds also provide a worst-case-performance guarantee which is crucial to optimizing LDPC codes for extremely low error rate applications, e.g., optical/satellite communicatio ns. Extensive numerical experiments are conducted that include both randomly and algebraically constructed LDPC codes, the results of which demonstrate the superior efficiency of the exhaustion algor ithm and its significant value for finite length code optimization .

Includes members:
  Member "code annealing and the suppressing effect of the cyclically lifted ldpc code ensemble":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "design of finite length irregular protograph codes with low error floors over the binary input awgn channel using cyclic liftings")
  Member "instanton analysis of low density parity check codes in the error floor regime":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the admm penalized decoder for ldpc codes")
  Member "tightened upper bounds on the ml decoding error probability of binary linear block codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bp led decoding algorithm for ldpc codes over awgn channels")
  Member "on the stopping distance and the stopping redundancy of codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "decoder tailored polar code design using the genetic algorithm")
  Member "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient qp admm decoder for binary ldpc codes and its performance analysis")
  Member "density evolution for asymmetric memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "asymptotic analysis on ldpc bicm scheme for compute and forward relaying")
  Member "on the complexity of finding stopping distance in tanner graphs":
    also connected through other hyperedges:
      (none)

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)