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 hardness of approximating stopping and trapping sets in ldpc codes"):
  Abstract: We prove that approximating the size of stopping and trapping sets in Tanner graphs of linear block codes, and more restrictively, the class of low-density parity-check (LDPC) codes, is NP-hard. The ramifications of our findings are that methods used for estimating the height of the error-floor of moderate- and long-length LDPC codes based on stopping and trapping set enumeration cannot provide accurate worst-case performance predictions.

Includes members:
  Member "error correction capability of column weight three ldpc codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "thresholds of absorbing sets in low density parity check codes")
  Member "error correction capability of column weight three ldpc codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "thresholds of absorbing sets in low density parity check codes")
  Member "instanton analysis of low density parity check codes in the error floor regime":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the admm penalized decoder for ldpc codes")
  Member "a general method for finding low error rates of ldpc codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hardness results on finding leafless elementary trapping sets and elementary absorbing sets of ldpc codes")
  Member "exhausting error prone patterns in ldpc codes":
    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)