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: "codes that achieve capacity on symmetric channels"
  Abstract: Transmission of information reliably and efficiently across channels is one of the fundamental goals of coding and information theory. In this respect, efficiently decodable deterministic coding schemes which achieve capacity provably have been elusive until as recent as 2008, even though schemes which come close to it in practice existed. This survey tries to give the interested reader an overview of the area. #R##N#Erdal Arikan came up with his landmark polar coding shemes which achieve capacity on symmetric channels subject to the constraint that the input codewords are equiprobable. His idea is to convert any B-DMC into efficiently encodable-decodable channels which have rates 0 and 1, while conserving capacity in this transformation. An exponentially decreasing probability of error which independent of code rate is achieved for all rates lesser than the symmetric capacity. These codes perform well in practice since encoding and decoding complexity is O(N log N). Guruswami et al. improved the above results by showing that error probability can be made to decrease doubly exponentially in the block length. #R##N#We also study recent results by Urbanke et al. which show that 2-transitive codes also achieve capacity on erasure channels under MAP decoding. Urbanke and his group use complexity theoretic results in boolean function analysis to prove that EXIT functions, which capture the error probability, have a sharp threshold at 1-R, thus proving that capacity is achieved. One of the oldest and most widely used codes - Reed Muller codes are 2-transitive. Polar codes are 2-transitive too and we thus have a different proof of the fact that they achieve capacity, though the rate of polarization would be better as found out by Guruswami.

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)