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 "performance complexity analysis for mac ml based decoding with user selection"):
  Abstract: This work explores the rate-reliability-complexity limits of the quasi-static K-user multiple access channel (MAC), with or without feedback. Using high-SNR asymptotics, the work first derives bounds on the computational resources required to achieve near-optimal (ML-based) decoding performance. It then bounds the (reduced) complexity needed to achieve any (including suboptimal) diversity-multiplexing performance tradeoff (DMT) performance, and finally bounds the same complexity, in the presence of feedback-aided user selection. This latter effort reveals the ability of a few bits of feedback not only to improve performance, but also to reduce complexity. In this context, the analysis reveals the interesting finding that proper calibration of user selection can allow for near-optimal ML-based decoding, with complexity that need not scale exponentially in the total number of codeword bits. The derived bounds constitute the best known performance-vs-complexity behavior to date for ML-based MAC decoding, as well as a first exploration of the complexity-feedback-performance interdependencies in multiuser settings.

Includes members:
  Member "achieving a vanishing snr gap to exact lattice decoding at a subexponential complexity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "deterministic sampling decoding where sphere decoding meets lattice gaussian distribution")
  Member "minimizing the complexity of fast sphere decoding of stbcs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on fast decodable algebraic space time codes")
  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-4 (induced by source paper "deterministic sampling decoding where sphere decoding meets lattice gaussian distribution")
  Member "lr aided mmse lattice decoding is dmt optimal for all approximately universal codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "markov chain monte carlo methods for lattice gaussian sampling lattice reduction and decoding optimization")
  Member "approximately universal optimality over several dynamic and non dynamic cooperative diversity schemes for wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "wireless coded caching a topological perspective")

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)