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: "effects of the lll reduction on the success probability of the babai point and on the complexity of sphere decoding"
  Abstract: The common method to estimate an unknown integer parameter vector in a linear model is to solve an integer least squares (ILS) problem. A typical approach to solving an ILS problem is sphere decoding. To make a sphere decoder faster, the well-known LLL reduction is often used as preprocessing. The Babai point produced by the Babai nearest plan algorithm is a suboptimal solution of the ILS problem. First we prove that the success probability of the Babai point as a lower bound on the success probability of the ILS estimator is sharper than the lower bound given by Hassibi and Boyd [1]. Then we show rigorously that applying the LLL reduction algorithm will increase the success probability of the Babai point. Finally we show rigorously that applying the LLL reduction algorithm will also reduce the computational complexity of sphere decoders, which is measured approximately by the number of nodes in the search tree in the literature

Connected through hyperedge HE-1 (induced by source paper "closed form word error rate analysis for successive interference cancellation decoders"):
  with members:
    - "effects of some lattice reductions on the success probability of the zero forcing decoder"
    - "success probability of the babai estimators for box constrained integer linear models"
    - "column reordering for box constrained integer least squares problems"
    - "lll reduction achieves the receive diversity in mimo decoding"
    - "on the limitations of the naive lattice decoding"

Connected through hyperedge HE-2 (induced by source paper "effects of some lattice reductions on the success probability of the zero forcing decoder"):
  with members:
    - "success probability of the babai estimators for box constrained integer linear models"
    - "lll reduction achieves the receive diversity in mimo decoding"

Connected through hyperedge HE-3 (induced by source paper "on the kz reduction"):
  with members:
    - "effects of some lattice reductions on the success probability of the zero forcing decoder"
    - "boosted kz and lll algorithms"
    - "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"
    - "on the proximity factors of lattice reduction aided decoding"

Connected through hyperedge HE-4 (induced by source paper "a kz reduction algorithm"):
  with members:
    - "effects of some lattice reductions on the success probability of the zero forcing decoder"
    - "an efficient optimal algorithm for the successive minima problem"
    - "success probability of the babai estimators for box constrained integer linear models"
    - "successive integer forcing and its sum rate optimality"
    - "integer forcing mimo linear receivers based on lattice reduction"
    - "on the proximity factors of lattice reduction aided decoding"
    - "improved analysis of kannan s shortest lattice vector algorithm"

Connected through hyperedge HE-5 (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"
    - "on the proximity factors of lattice reduction aided decoding"
    - "decoding by sampling a randomized lattice algorithm for bounded distance decoding"
    - "integer forcing linear receivers"
    - "compute and forward harnessing interference through structured codes"

Connected through hyperedge HE-6 (induced by source paper "on the success probability of the box constrained rounding and babai detectors"):
  with members:
    - "effects of some lattice reductions on the success probability of the zero forcing decoder"

Connected through hyperedge HE-7 (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"
    - "on the kz reduction"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "asymptotic analysis of random lattices in high dimensions"):
  with members:
    - "fifty years of mimo detection the road to large scale mimos"
    - "sphere decoding complexity exponent for decoding full rate codes over the quasi static mimo channel"
    - "faster recursions in sphere decoding"
    - "finite snr diversity multiplexing tradeoff via asymptotic analysis of large mimo systems"
    - "asymptotic performance of linear receivers in mimo fading channels"
    - "on the limitations of the naive lattice 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)