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: "weight distributions of regular low density parity check codes over finite fields"
  Abstract: The average weight distribution of a regular low-density parity-check (LDPC) code ensemble over a finite field is thoroughly analyzed. In particular, a precise asymptotic approximation of the average weight distribution is derived for the small-weight case, and a series of fundamental qualitative properties of the asymptotic growth rate of the average weight distribution are proved. Based on this analysis, a general result, including all previous results as special cases, is established for the minimum distance of individual codes in a regular LDPC code ensemble.

Connected through hyperedge HE-1 (induced by source paper "bp led decoding algorithm for ldpc codes over awgn channels"):
  with members:
    - "new techniques for upper bounding the ml decoding performance of binary linear codes"
    - "tree structure expectation propagation for decoding ldpc codes over binary erasure channels"
    - "lowering the error floor of ldpc codes using cyclic liftings"
    - "tightened upper bounds on the ml decoding error probability of binary linear block codes"

Connected through hyperedge HE-2 (induced by source paper "distance verification for classical and quantum ldpc codes"):
  with members:
    - "quantum ldpc codes with positive rate and minimum distance proportional to the square root of the blocklength"
    - "quantum error correction beyond the bounded distance decoding limit"
    - "a construction of quantum ldpc codes from cayley graphs"

Connected through hyperedge HE-3 (induced by source paper "constructing linear encoders with good spectra"):
  with members:
    - "finite length scaling for polar codes"
    - "polar codes speed of polarization and polynomial gap to capacity"
    - "good random matrices over finite fields"
    - "second order weight distributions"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "linear codes based lossless joint source channel coding for multiple access channels"
    - "a rank metric approach to error control in random network coding"

Connected through hyperedge HE-4 (induced by source paper "spectral shape of doubly generalized ldpc codes efficient and exact evaluation"):
  with members:
    - "weight distributions of multi edge type ldpc codes"
    - "on the growth rate of the weight distribution of irregular doubly generalized ldpc codes"
    - "on a class of doubly generalized ldpc codes with single parity check variable nodes"
    - "doubly generalized ldpc codes stability bound over the bec"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"

Connected through hyperedge HE-5 (induced by source paper "second order weight distributions"):
  with members:
    - "good random matrices over finite fields"
    - "linear codes based lossless joint source channel coding for multiple access channels"
    - "growth rate of the weight distribution of doubly generalized ldpc codes general case and efficient evaluation"
    - "on the asymptotic weight and stopping set distribution of regular ldpc ensembles"

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)