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: "searching for voltage graph based ldpc tailbiting codes with large girth"
  Abstract: The relation between parity-check matrices of quasi-cyclic (QC) low-density parity-check (LDPC) codes and biadjacency matrices of bipartite graphs supports searching for powerful LDPC block codes. Using the principle of tailbiting, compact representations of bipartite graphs based on convolutional codes can be found. Bounds on the girth and the minimum distance of LDPC block codes constructed in such a way are discussed. Algorithms for searching iteratively for LDPC block codes with large girth and for determining their minimum distance are presented. Constructions based on all-one matrices, Steiner Triple Systems, and QC block codes are introduced. Finally, new QC regular LDPC block codes with girth up to 24 are given.

Connected through hyperedge HE-1 (induced by source paper "integer ring sieve irs for constructing compact qc ldpc codes with large girth"):
  with members:
    - "efficient search of compact qc ldpc and sc ldpc convolutional codes with large girth"
    - "compact qc ldpc block and sc ldpc convolutional codes for low latency communications"
    - "design and analysis of time invariant sc ldpc convolutional codes with small constraint length"
    - "the cycle consistency matrix approach to absorbing sets in separable circulant based ldpc codes"
    - "lowering the error floor of ldpc codes using cyclic liftings"

Connected through hyperedge HE-2 (induced by source paper "compact qc ldpc block and sc ldpc convolutional codes for low latency communications"):
  with members:
    - "efficient search of compact qc ldpc and sc ldpc convolutional codes with large girth"
    - "design and analysis of time invariant sc ldpc convolutional codes with small constraint length"
    - "lower bounds on the lifting degree of single edge and multiple edge qc ldpc codes by difference matrices"
    - "terminated ldpc convolutional codes with thresholds close to capacity"

Connected through hyperedge HE-3 (induced by source paper "a new multi edge metric constrained peg algorithm for designing binary ldpc code with improved cycle structure"):
  with members:
    - "quasi cyclic ldpc codes based on pre lifted protographs"
    - "design of multiple edge protographs for qc ldpc codes avoiding short inevitable cycles"
    - "design of finite length irregular protograph codes with low error floors over the binary input awgn channel using cyclic liftings"

Connected through hyperedge HE-4 (induced by source paper "efficient search of compact qc ldpc and sc ldpc convolutional codes with large girth"):
  with members:
    - "design and analysis of time invariant sc ldpc convolutional codes with small constraint length"
    - "lower bounds on the lifting degree of single edge and multiple edge qc ldpc codes by difference matrices"
    - "terminated ldpc convolutional codes with thresholds close to capacity"

Connected through hyperedge HE-5 (induced by source paper "design and analysis of time invariant sc ldpc codes with small constraint length"):
  with members:
    - "on advisability of designing short length qc ldpc codes using perfect difference families"
    - "spatially coupled ldpc codes constructed from protographs"
    - "hierarchical and high girth qc ldpc codes"
    - "construction of high rate regular quasi cyclic ldpc codes based on cyclic difference families"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "progressive differences convolutional low density parity check codes"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "deriving good ldpc convolutional codes from ldpc block codes"

Connected through hyperedge HE-6 (induced by source paper "from cages to trapping sets and codewords a technique to derive tight upper bounds on the minimum size of trapping sets and minimum distance of ldpc codes"):
  with members:
    - "on the minimum stopping distance of array low density parity check codes"
    - "on characterization of elementary trapping sets of variable regular ldpc codes"
    - "practical graph isomorphism ii"
    - "quasi cyclic ldpc codes influence of proto and tanner graph structure on minimum hamming distance upper bounds"
    - "error floor approximation for ldpc codes in the awgn channel"
    - "lowering the error floor of ldpc codes using cyclic liftings"
    - "on the dynamics of the error floor behavior in regular ldpc codes"
    - "shortened array codes of large girth"

Connected through hyperedge HE-7 (induced by source paper "ml and near ml decoding of ldpc codes over the bec bounds and decoding algorithms"):
  with members:
    - "distance properties of short ldpc codes and their impact on the bp ml and near ml decoding performance"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "decoding of convolutional codes over the erasure channel"
    - "analysis of quasi cyclic ldpc codes under ml decoding over the erasure channel"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"

Connected through hyperedge HE-8 (induced by source paper "time invariant spatially coupled low density parity check codes with small constraint length"):
  with members:
    - "array convolutional low density parity check codes"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "quasi cyclic ldpc codes influence of proto and tanner graph structure on minimum hamming distance upper bounds"
    - "progressive differences convolutional low density parity check codes"
    - "deriving good ldpc convolutional codes from ldpc block codes"

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)