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 the weight distribution of convolutional codes"
  Abstract: Detailed information about the weight distribution of a convolutional code is given by the adjacency matrix of the state diagram associated with a controller canonical form of the code. We will show that this matrix is an invariant of the code. Moreover, it will be proven that codes with the same adjacency matrix have the same dimension and the same Forney indices and finally that for one-dimensional binary convolutional codes the adjacency matrix determines the code uniquely up to monomial equivalence.

Connected through hyperedge HE-1 (induced by source paper "on the macwilliams identity for classical and quantum convolutional codes"):
  with members:
    - "correction of data and syndrome errors by stabilizer codes"
    - "normal factor graphs and holographic transformations"
    - "a macwilliams identity for convolutional codes the general case"
    - "on the macwilliams identity for convolutional codes"

Connected through hyperedge HE-2 (induced by source paper "on isometries for convolutional codes"):
  with members:
    - "a macwilliams identity for convolutional codes the general case"
    - "on the macwilliams identity for convolutional codes"
    - "state space realizations and monomial equivalence for convolutional codes"

Connected through hyperedge HE-3 (induced by source paper "receding horizon decoding of convolutional codes"):
  with members:
    - "algebraic decoding for doubly cyclic convolutional codes"

Connected through hyperedge HE-4 (induced by source paper "a macwilliams identity for convolutional codes the general case"):
  with members:
    - "on the macwilliams identity for convolutional codes"
    - "the dynamics of group codes dual abelian group codes and systems"

Connected through hyperedge HE-5 (induced by source paper "superregular matrices and the construction of convolutional codes having a maximum distance profile"):
  with members:
    - "on the macwilliams identity for convolutional codes"
    - "a class of one dimensional mds convolutional codes"

Connected through hyperedge HE-6 (induced by source paper "on the macwilliams identity for convolutional codes"):
  with members:
    - "the dynamics of group codes dual abelian group codes and systems"

Connected through hyperedge HE-7 (induced by source paper "state space realizations and monomial equivalence for convolutional codes"):
  with members:
    - "on the macwilliams identity for convolutional 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)