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: "lattice gaussian sampling by markov chain monte carlo convergence rate and decoding complexity"
  Abstract: Sampling from the lattice Gaussian distribution is an efficient way for solving the closest vector problem (CVP) in lattice decoding. In this paper, decoding by MCMC-based lattice Gaussian sampling is investigated in full details. First of all, the spectral gap of the transition matrix of the Markov chain induced by the independent Metropolis-Hastings-Klein (MHK) algorithm is derived, dictating an exponential convergence rate to the target lattice Gaussian distribution. Then, the decoding complexity of CVP is derived as $O(e^{d^2(\Lambda, \mathbf{c})/\min_i^2\|\widehat{\mathbf{b}}_i\|})$, where $d(\Lambda, \mathbf{c})$ represents the Euclidean distance between the query point $\mathbf{c}$ and the lattice $\Lambda$, and $\mathbf{\widehat{b}}_i$ is the $i$th Gram-Schmidt vector of the lattice basis $\mathbf{B}$. Furthermore, the decoding radius from the perspective of bounded distance decoding (BDD) given a fixed number of Markov moves $t$ is also derived, revealing a flexible trade-off between the decoding performance and complexity. Finally, by taking advantages of $k$ trial samples from the proposal distribution, the independent multiple-try Metropolis-Klein (MTMK) algorithm is proposed to further enhance the exponential convergence rate. By adjusting $k$, the independent MTMK sampler enjoys a flexible decoding performance, where the independent MHK algorithm is just a case with $k=1$. Additionally, the proposed decoding allows a fully parallel implementation, which is beneficial for the practical interest.

Connected through hyperedge HE-1 (induced by source paper "on the geometric ergodicity of metropolis hastings algorithms for lattice gaussian sampling"):
  with members:
    - "discrete gaussian sampling reduces to cvp and svp"
    - "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time"
    - "markov chain monte carlo algorithms for lattice gaussian sampling"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "achieving awgn channel capacity with lattice gaussian coding"
    - "decoding by sampling part ii derandomization and soft output decoding"
    - "on the proximity factors of lattice reduction aided decoding"
    - "decoding by sampling a randomized lattice algorithm for bounded distance decoding"

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)