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: "boosted kz and lll algorithms"
  Abstract: There exist two issues among popular lattice reduction algorithms that should cause our concern. The first one is Korkine–Zolotarev (KZ) and Lenstra–Lenstra–Lovasz (LLL) algorithms may increase the lengths of basis vectors. The other is KZ reduction suffers worse performance than Minkowski reduction in terms of providing short basis vectors, despite its superior theoretical upper bounds. To address these limitations, we improve the size reduction steps in KZ and LLL to set up two new efficient algorithms, referred to as boosted KZ and LLL, for solving the shortest basis problem with exponential and polynomial complexity, respectively. Both of them offer better actual performance than their classic counterparts, and the performance bounds for KZ are also improved. We apply them to designing integer-forcing (IF) linear receivers for multi-input multioutput communications. Our simulations confirm their rate and complexity advantages.

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"
    - "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"
    - "decoding by sampling a randomized lattice algorithm for bounded distance decoding"
    - "communication over mimo broadcast channels using lattice basis reduction"

Connected through hyperedge HE-2 (induced by source paper "steepest gradient based orthogonal precoder for integer forcing mimo"):
  with members:
    - "efficient integer coefficient search for compute and forward"
    - "encoding and indexing of lattice codes"
    - "an efficient optimal algorithm for the successive minima problem"
    - "full diversity unitary precoded integer forcing"
    - "precoded integer forcing universally achieves the mimo capacity to within a constant gap"
    - "integer forcing mimo linear receivers based on lattice reduction"
    - "integer forcing linear receivers"
    - "mimo precoding with x and y codes"

Connected through hyperedge HE-3 (induced by source paper "improved upper bounds on the hermite and kz constants"):
  with members:
    - "on the kz reduction"
    - "successive integer forcing and its sum rate optimality"
    - "decoding by embedding correct decoding radius and dmt optimality"
    - "integer forcing mimo linear receivers based on lattice reduction"
    - "on the proximity factors of lattice reduction aided decoding"

Connected through hyperedge HE-4 (induced by source paper "markov chain monte carlo methods for lattice gaussian sampling convergence analysis and enhancement"):
  with members:
    - "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"
    - "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time"

Connected through hyperedge HE-5 (induced by source paper "markov chain monte carlo methods for lattice gaussian sampling lattice reduction and decoding optimization"):
  with members:
    - "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"
    - "decoding by sampling part ii derandomization and soft output decoding"

Connected through hyperedge HE-6 (induced by source paper "ring compute and forward over block fading channels"):
  with members:
    - "polynomially solvable instances of the shortest and closest vector problems with applications to compute and forward"
    - "algebraic lattice codes achieve the capacity of the compound block fading channel"
    - "universal lattice codes for mimo channels"
    - "adaptive compute and forward with lattice codes over algebraic integers"
    - "construction a of lattices over number fields and block fading wiretap coding"
    - "lattices over eisenstein integers for compute and forward"
    - "an algebraic approach to physical layer network coding"
    - "the approximate sum capacity of the symmetric gaussian k user interference channel"

Connected through hyperedge HE-7 (induced by source paper "hybrid vector perturbation precoding the blessing of approximate message passing"):
  with members:
    - "hybrid approximate message passing"
    - "on the performance of mismatched data detection in large mimo systems"
    - "channel pre inversion and max sinr vector perturbation for large scale broadcast channels"
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "optimality of large mimo detection via approximate message passing"
    - "massive mimo for next generation wireless systems"
    - "expectation propagation for approximate bayesian inference"
    - "proximity factors of lattice reduction aided precoding for multiantenna broadcast"

Connected through hyperedge HE-8 (induced by source paper "on the kz reduction"):
  with members:
    - "effects of some lattice reductions on the success probability of the zero forcing decoder"
    - "a modified kz reduction algorithm"
    - "success probability of the babai estimators for box constrained integer linear models"
    - "successive integer forcing and its sum rate optimality"
    - "decoding by embedding correct decoding radius and dmt optimality"
    - "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"

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)