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: "why we can not surpass capacity the matching condition"
  Abstract: We show that iterative coding systems can not surpass capacity using only quantities which naturally appear in density evolution. Although the result in itself is trivial, the method which we apply shows that in order to achieve capacity the various components in an iterative coding system have to be perfectly matched. This generalizes the perfect matching condition which was previously known for the case of transmission over the binary erasure channel to the general class of binary-input memoryless output-symmetric channels. Potential applications of this perfect matching condition are the construction of capacity-achieving degree distributions and the determination of the number required iterations as a function of the multiplicative gap to capacity.

Connected through hyperedge HE-1 (induced by source paper "iterative decoding performance bounds for ldpc codes on noisy channels"):
  with members:
    - "capacity achieving codes with bounded graphical complexity on noisy channels"
    - "capacity achieving ensembles for the binary erasure channel with bounded complexity"

Connected through hyperedge HE-2 (induced by source paper "performance versus complexity per iteration for low density parity check codes an information theoretic approach"):
  with members:
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "accumulate repeat accumulate codes systematic codes achieving the binary erasure channel capacity with bounded complexity"
    - "capacity achieving codes with bounded graphical complexity on noisy channels"
    - "improved bounds on the parity check density and achievable rates of binary linear block codes with applications to ldpc codes"
    - "on achievable rates and complexity of ldpc codes for parallel channels with application to puncturing"
    - "capacity achieving ensembles for the binary erasure channel with bounded complexity"
    - "life above threshold from list decoding to area theorem and mse"
    - "bounds on the decoding complexity of punctured codes on 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)