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: "nearly accurate solutions for ising like models using maximal entropy random walk"
  Abstract: While one-dimensional Markov processes are well understood, going to higher dimensions there are only a few analytically solved Ising-like models, in practice requiring to use relatively costly, uncontrollable and inaccurate Monte-Carlo methods. There is discussed analytical approach for e.g. $width\times \infty$ approximation of lattice, also exploiting Hammersley-Clifford theorem to generate random Gibbs/Markov field through scanning line-by-line using local statistical model as in lossless image compression. While its conditional distributions could be found with Monte-Carlo methods, there is discussed use of Maximal Entropy Random Walk (MERW) to calculate them from approximation of lattice as infinite in one direction and finite in the remaining. Specifically, in the finite directions there is built alphabet of all patterns, then transition matrix containing energy for all pairs of such patterns is built, from its dominant eigenvector getting probability distribution of pairs of patterns in Boltzmann distribution of their infinite sequences, which can be translated into local statistical model for line-by-line scan. Such inexpensive models, requiring seconds on a laptop for attached implementation and directly providing probability distributions of patterns, were tested for mean entropy and energy per node, getting maximal $\approx 0.02$ error from analytical solution near critical point, which quickly improves to extremely accurate e.g. $\approx 10^{-10}$ error for $J\approx 0.2$.

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)