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: "analytical solution of covariance evolution for irregular ldpc codes"
  Abstract: The scaling law developed by Amraoui et al. is a powerful technique to estimate the block erasure probabilities of finite- length low-density parity-check (LDPC) codes. Solving a system of differential equations called covariance evolution, one can obtain the scaling parameter. However, the covariance evolution has not been analytically solved. In this paper, we present the analytical solution of the covariance evolution for irregular LDPC code ensembles.

Connected through hyperedge HE-1 (induced by source paper "a scaling law to predict the finite length performance of spatially coupled ldpc codes"):
  with members:
    - "spatially coupled sparse codes on graphs theory and practice"
    - "on the convergence speed of spatially coupled ldpc ensembles"
    - "on the minimum distance of generalized spatially coupled ldpc codes"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "exact free distance and trapping set growth rates for ldpc convolutional codes"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "scaling behavior of convolutional ldpc ensembles over the bec"

Connected through hyperedge HE-2 (induced by source paper "improving the finite length performance of spatially coupled ldpc codes by connecting multiple code chains"):
  with members:
    - "new codes on graphs constructed by connecting spatially coupled chains"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "exact free distance and trapping set growth rates for ldpc convolutional codes"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "connecting spatially coupled ldpc code chains"
    - "improvement of bp based cdma multiuser detection by spatial coupling"
    - "scaling behavior of convolutional ldpc ensembles over the bec"

Connected through hyperedge HE-3 (induced by source paper "tree structure expectation propagation for ldpc decoding over the bec"):
  with members:
    - "expectation propagation for approximate bayesian inference"
    - "structured region graphs morphing ep into gbp"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "tree structure expectation propagation for decoding ldpc codes over binary erasure channels"
    - "accumulate repeat accumulate codes capacity achieving ensembles of systematic codes for the erasure channel with bounded complexity"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "on the stopping distance and the stopping redundancy of codes"
    - "finite length scaling for iteratively decoded ldpc ensembles"

Connected through hyperedge HE-4 (induced by source paper "analysis of the second moment of the lt decoder"):
  with members:
    - "analytical solution of covariance evolution for regular ldpc codes"
    - "finite length scaling for iteratively decoded 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)