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 "achieving a vanishing snr gap to exact lattice decoding at a subexponential complexity"):
  Abstract: This study identifies the first lattice decoding solution that achieves, in the general outage-limited multiple-input multiple-output (MIMO) setting and in the high-rate and high-signal-to-noise ratio limit, both a vanishing gap to the error performance of the exact solution of regularized lattice decoding, as well as a computational complexity that is subexponential in the number of codeword bits and in the rate. The proposed solution employs Lenstra-Lenstra-Lovasz-based lattice reduction (LR)-aided regularized (lattice) sphere decoding and proper timeout policies. These performance and complexity guarantees hold for most MIMO scenarios, most fading statistics, all channel dimensions, and all full-rate lattice codes. In sharp contrast to the aforementioned very manageable complexity, the complexity of other standard preprocessed lattice decoding solutions is revealed here to be extremely high. Specifically, this study has quantified the complexity of regularized lattice (sphere) decoding and has proved that the computational resources required by this decoder to achieve a good rate-reliability performance are exponential in the lattice dimensionality and in the number of codeword bits, and it in fact matches, in common scenarios, the complexity of ML-based sphere decoders. Through this sharp contrast, this study was able to, for the first time, rigorously demonstrate and quantify the pivotal role of LR as a special complexity reducing ingredient.

Includes members:
  Member "on the proximity factors of lattice reduction aided decoding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on low complexity lattice reduction algorithms for large scale mimo detection the blessing of sequential reduction")
  Member "efficient soft input soft output tree detection via an improved path metric":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fifty years of mimo detection the road to large scale mimos")
  Member "sphere decoding complexity exponent for decoding full rate codes over the quasi static mimo channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deterministic sampling decoding where sphere decoding meets lattice gaussian distribution")
  Member "decoding by sampling a randomized lattice algorithm for bounded distance decoding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on low complexity lattice reduction algorithms for large scale mimo detection the blessing of sequential reduction")
  Member "faster recursions in sphere decoding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to detect")
  Member "finite snr diversity multiplexing tradeoff via asymptotic analysis of large mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "short packet communications over multiple antenna rayleigh fading channels")
  Member "mimo detection for high order qam based on a gaussian tree approximation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi user detection based on expectation propagation for the non coherent simo multiple access channel")
  Member "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)