human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "independent metropolis hastings klein algorithm for lattice gaussian sampling":
    also connected through other hyperedges:
      (none)
  Member "solving the closest vector problem in 2 n time the discrete gaussian strikes again":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hardness of bounded distance decoding on lattices in ell_p norms")
  Member "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deterministic sampling decoding where sphere decoding meets lattice gaussian distribution")
  Member "markov chain monte carlo algorithms for lattice gaussian sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deterministic sampling decoding where sphere decoding meets lattice gaussian distribution")
  Member "massive mimo for next generation wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint power control and lsfd for wireless powered cell free massive mimo")
  Member "semantically secure lattice codes for the gaussian wiretap channel":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semantically secure lattice codes for compound mimo channels")
  Member "on the closest vector problem with a distance guarantee":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hardness of bounded distance decoding on lattices in ell_p norms")
  Member "decoding by embedding correct decoding radius and dmt optimality":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deterministic sampling decoding where sphere decoding meets lattice gaussian distribution")

Question: Please tell me which class the source paper 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)