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 "asymptotic analysis of random lattices in high dimensions"):
  Abstract: This paper presents the asymptotic analysis of random lattices in high dimensions to clarify the distance properties of the considered lattices. These properties not only indicate the asymptotic value for the distance between any pair of lattice points in high-dimension random lattices, but also describe the convergence behavior of how the asymptotic value approaches the exact distance. The asymptotic analysis further prompts new insights into the asymptotic behavior of sphere-decoding complexity and the pairwise error probability (PEP) with maximum-likelihood (ML) detector for a large number of antennas.

Includes members:
  Member "fifty years of mimo detection the road to large scale mimos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "model driven deep learning for joint mimo channel estimation and signal detection")
  Member "effects of the lll reduction on the success probability of the babai point and on the complexity of sphere decoding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "closed form word error rate analysis for successive interference cancellation decoders")
  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 "faster recursions in sphere decoding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "short packet communications over multiple antenna rayleigh fading channels")
  Member "asymptotic performance of linear receivers in mimo fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "comments on physical layer cryptography through massive mimo")
  Member "on the limitations of the naive lattice decoding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "markov chain monte carlo methods for lattice gaussian sampling lattice reduction and decoding optimization")

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)