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 "m ary detection and q ary decoding in large scale mimo a non binary belief propagation approach"):
  Abstract: In this paper, we propose a non-binary belief propagation approach (NB-BP) for detection of $M$-ary modulation symbols and decoding of $q$-ary LDPC codes in large-scale multiuser MIMO systems. We first propose a message passing based symbol detection algorithm which computes vector messages using a scalar Gaussian approximation of interference, which results in a total complexity of just $O(KN\sqrt{M})$, where $K$ is the number of uplink users and $N$ is the number of base station (BS) antennas. The proposed NB-BP detector does not need to do a matrix inversion, which gives a complexity advantage over MMSE detection. We then design optimized $q$-ary LDPC codes by matching the EXIT charts of the proposed detector and the LDPC decoder. Simulation results show that the proposed NB-BP detection-decoding approach using the optimized LDPC codes achieve significantly better performance (by about 1 dB to 7 dB at $10^{-5}$ coded BER for various system loading factors with number of users ranging from 16 to 128 and number of BS antennas fixed at 128) compared to using linear detectors (e.g., MMSE detector) and off-the-shelf $q$-ary irregular LDPC codes. Also, even with estimated channel knowledge (e.g., with MMSE channel estimate), the performance of the proposed NB-BP detector is better than that of the MMSE detector.

Includes members:
  Member "scaling up mimo opportunities and challenges with very large arrays":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")
  Member "low complexity detection in large dimension mimo isi channels using graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deterministic sampling decoding where sphere decoding meets lattice gaussian distribution")
  Member "mimo detection for high order qam based on a gaussian tree approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi user detection based on expectation propagation for the non coherent simo multiple access channel")
  Member "design and analysis of nonbinary ldpc codes for arbitrary discrete memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lattice coding for downlink multiuser transmission")

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)