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: "decoding by sampling a randomized lattice algorithm for bounded distance decoding"
  Abstract: Despite its reduced complexity, lattice reduction-aided decoding exhibits a widening gap to maximum-likelihood (ML) performance as the dimension increases. To improve its performance, this paper presents randomized lattice decoding based on Klein's sampling technique, which is a randomized version of Babai's nearest plane algorithm [i.e., successive interference cancelation (SIC)] and samples lattice points from a Gaussian-like distribution over the lattice. To find the closest lattice point, Klein's algorithm is used to sample some lattice points and the closest among those samples is chosen. Lattice reduction increases the probability of finding the closest lattice point, and only needs to be run once during preprocessing. Further, the sampling can operate very efficiently in parallel. The technical contribution of this paper is twofold: we analyze and optimize the decoding radius of sampling decoding resulting in better error performance than Klein's original algorithm, and propose a very efficient implementation of random rounding. Of particular interest is that a fixed gain in the decoding radius compared to Babai's decoding can be achieved at polynomial complexity. The proposed decoder is useful for moderate dimensions where sphere decoding becomes computationally intensive, while lattice reduction-aided decoding starts to suffer considerable loss. Simulation results demonstrate near-ML performance is achieved by a moderate number of samples, even if the dimension is as high as 32.

Connected through hyperedge HE-1 (induced by source paper "on low complexity lattice reduction algorithms for large scale mimo detection the blessing of sequential reduction"):
  with members:
    - "on the kz reduction"
    - "boosted kz and lll algorithms"
    - "gfclll a greedy selection based approach for fixed complexity lll reduction"
    - "on the geometric ergodicity of metropolis hastings algorithms for lattice gaussian sampling"
    - "success probability of the babai estimators for box constrained integer linear models"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "on the proximity factors of lattice reduction aided decoding"
    - "communication over mimo broadcast channels using lattice basis reduction"

Connected through hyperedge HE-2 (induced by source paper "deterministic sampling decoding where sphere decoding meets lattice gaussian distribution"):
  with members:
    - "lattice gaussian sampling by markov chain monte carlo bounded distance decoding and trapdoor sampling"
    - "on the geometric ergodicity of metropolis hastings algorithms for lattice gaussian sampling"
    - "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"
    - "massive mimo for next generation wireless systems"
    - "decoding by embedding correct decoding radius and dmt optimality"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-3 (induced by source paper "markov chain monte carlo methods for lattice gaussian sampling convergence analysis and enhancement"):
  with members:
    - "boosted kz and lll algorithms"
    - "lattice gaussian sampling by markov chain monte carlo bounded distance decoding and trapdoor sampling"
    - "sensitivity gains by mismatched probabilistic shaping for optical communication systems"
    - "polar codes and polar lattices for independent fading channels"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "achieving secrecy capacity of the gaussian wiretap channel with polar lattices"
    - "fifty years of mimo detection the road to large scale mimos"
    - "on the geometric ergodicity of metropolis hastings algorithms for lattice gaussian sampling"

Connected through hyperedge HE-4 (induced by source paper "markov chain monte carlo methods for lattice gaussian sampling lattice reduction and decoding optimization"):
  with members:
    - "boosted kz and lll algorithms"
    - "polar codes and polar lattices for independent fading channels"
    - "scan order in gibbs sampling models in which it matters and bounds on how much"
    - "achieving secrecy capacity of the gaussian wiretap channel with polar lattices"
    - "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"
    - "decoding by embedding correct decoding radius and dmt optimality"

Connected through hyperedge HE-5 (induced by source paper "lattice gaussian sampling by markov chain monte carlo convergence rate and decoding complexity"):
  with members:
    - "independent metropolis hastings klein algorithm for lattice gaussian sampling"
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "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"
    - "massive mimo for next generation wireless systems"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "on the closest vector problem with a distance guarantee"
    - "decoding by embedding correct decoding radius and dmt optimality"

Connected through hyperedge HE-6 (induced by source paper "boosted kz and lll algorithms"):
  with members:
    - "a modified kz reduction algorithm"
    - "integer forcing mimo linear receivers based on lattice reduction"
    - "effects of the lll reduction on the success probability of the babai point and on the complexity of sphere decoding"
    - "on the proximity factors of lattice reduction aided decoding"
    - "integer forcing linear receivers"
    - "compute and forward harnessing interference through structured codes"

Connected through hyperedge HE-7 (induced by source paper "on the geometric ergodicity of metropolis hastings algorithms for lattice gaussian sampling"):
  with members:
    - "lattice gaussian sampling by markov chain monte carlo convergence rate and decoding complexity"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "independent metropolis hastings klein algorithm for lattice gaussian sampling"):
  with members:
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "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"

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)