human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on distance properties of quasi cyclic protograph based ldpc codes"
  Abstract: Recent work [1][2] has shown that properly designed protograph-based LDPC codes may have minimum distance linearly increasing with block length. This notion rests on ensemble arguments over all possible expansions of the base protograph. When implementation complexity is considered, the expansion is typically chosen to be quite orderly. For example, protograph expansion by cyclically shifting connections creates a quasi-cyclic (QC) code. Other recent work [3] has provided upper bounds on the minimum distance of QC codes. In this paper, these bounds are expanded upon to cover puncturing and tightened in several specific cases. We then evaluate our upper bounds for the most prominent protograph code thus far, one proposed for deep-space usage in the CCSDS experimental standard [4], the code known as AR4JA.

Connected through hyperedge HE-1 (induced by source paper "quasi cyclic ldpc codes influence of proto and tanner graph structure on minimum hamming distance upper bounds"):
  with members:
    - "searching for voltage graph based ldpc tailbiting codes with large girth"
    - "deriving good ldpc convolutional codes from ldpc block codes"
    - "lowering the error floor of ldpc codes using cyclic liftings"
    - "absdet pseudo codewords and perm pseudo codewords definitions and properties"
    - "adaptive methods for linear programming decoding"
    - "pseudo codeword analysis of tanner graphs from projective and euclidean planes"
    - "a new construction for ldpc codes using permutation polynomials over integer rings"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes"

Connected through hyperedge HE-2 (induced by source paper "windowed decoding of protograph based ldpc convolutional codes over erasure channels"):
  with members:
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "asymptotically regular ldpc codes with linear distance growth and thresholds close to capacity"
    - "construction of near optimum burst erasure correcting low density parity check codes"
    - "improved upper bounds on stopping redundancy"

Connected through hyperedge HE-3 (induced by source paper "bounds on the minimum distance of punctured quasi cyclic ldpc codes"):
  with members:
    - "hierarchical and high girth qc ldpc codes"
    - "quasi cyclic ldpc codes influence of proto and tanner graph structure on minimum hamming distance upper bounds"

Connected through hyperedge HE-4 (induced by source paper "deriving good ldpc convolutional codes from ldpc block codes"):
  with members:
    - "quasi cyclic ldpc codes influence of proto and tanner graph structure on minimum hamming distance upper bounds"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "asymptotically regular ldpc codes with linear distance growth and thresholds close to capacity"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes"
    - "pseudocodewords of tanner graphs"

Question: Please tell me which class the center node 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)