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: "correction trees as an alternative to turbo codes and low density parity check codes"
  Abstract: The rapidly improving performance of modern hardware renders convolutional codes obsolete, and allows for the practical implementation of more sophisticated correction codes such as low density parity check (LDPC) and turbo codes (TC). Both are decoded by iterative algorithms, which require a disproportional computational effort for low channel noise. They are also unable to correct higher noise levels, still below the Shannon theoretical limit. In this paper, we discuss an enhanced version of a convolutional-like decoding paradigm which adopts very large spaces of possible system states, of the order of $2^{64}$. Under such conditions, the traditional convolution operation is rendered useless and needs to be replaced by a carefully designed state transition procedure. The size of the system state space completely changes the correction philosophy, as state collisions are virtually impossible and the decoding procedure becomes a correction tree. The proposed decoding algorithm is practically cost-free for low channel noise. As the channel noise approaches the Shannon limit, it is still possible to perform correction, although its cost increases to infinity. In many applications, the implemented decoder can essentially outperform both LDPC and TC. This paper describes the proposed correction paradigm and theoretically analyzes the asymptotic correction performance. The considered encoder and decoder were verified experimentally for the binary symmetric channel. The correction process remains practically cost-free for channel error rates below 0.05 and 0.13 for the 1/2 and 1/4 rate codes, respectively. For the considered resource limit, the output bit error rates reach the order of $10^{-3}$ for channel error rates 0.08 and 0.18. The proposed correction paradigm can be easily extended to other communication channels; the appropriate generalizations are also discussed in this study.

Connected through hyperedge HE-1 (induced by source paper "nonuniform probability modulation for reducing energy consumption of remote sensors"):
  with members:
    - "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding"
    - "embedding grayscale halftone pictures in qr codes using correction trees"
    - "optimal encoding on discrete lattice with translational invariant constrains using statistical algorithms"

Connected through hyperedge HE-2 (induced by source paper "embedding grayscale halftone pictures in qr codes using correction trees"):
  with members:
    - "asymmetric numeral systems"

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)