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: "on the kz reduction"
  Abstract: The Korkine-Zolotareff (KZ) reduction is one of the often used reduction strategies for lattice decoding. In this paper, we first investigate some important properties of KZ reduced matrices. Specifically, we present a linear upper bound on the Hermit constant which is around $\frac{7}{8}$ times of the existing sharpest linear upper bound, and an upper bound on the KZ constant which is {\em polynomially} smaller than the existing sharpest one. We also propose upper bounds on the lengths of the columns of KZ reduced matrices, and an upper bound on the orthogonality defect of KZ reduced matrices which are even {\em polynomially and exponentially} smaller than those of boosted KZ reduced matrices, respectively. Then, we derive upper bounds on the magnitudes of the entries of any solution of a shortest vector problem (SVP) when its basis matrix is LLL reduced. These upper bounds are useful for analyzing the complexity and understanding numerical stability of the basis expansion in a KZ reduction algorithm. Finally, we propose a new KZ reduction algorithm by modifying the commonly used Schnorr-Euchner search strategy for solving SVPs and the basis expansion method proposed by Zhang {\em et al.} Simulation results show that the new KZ reduction algorithm is much faster and more numerically reliable than the KZ reduction algorithm proposed by Zhang {\em et al.}, especially when the basis matrix is ill conditioned.

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:
    - "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"
    - "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 "improved upper bounds on the hermite and kz constants"):
  with members:
    - "boosted kz and lll algorithms"
    - "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-3 (induced by source paper "an efficient optimal algorithm for the successive minima problem"):
  with members:
    - "effects of some lattice reductions on the success probability of the zero forcing decoder"
    - "boosted kz and lll algorithms"
    - "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes"
    - "expanding the compute and forward framework unequal powers signal levels and multiple linear combinations"
    - "compute compress and forward exploiting asymmetry of wireless relay networks"
    - "full diversity unitary precoded integer forcing"
    - "success probability of the babai estimators for box constrained integer linear models"
    - "precoded integer forcing universally achieves the mimo capacity to within a constant gap"

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)