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 "decentralized equalization with feedforward architectures for massive mu mimo"):
  Abstract: Linear data-detection algorithms that build on zero forcing (ZF) or linear minimum mean-square error (L-MMSE) equalization achieve near-optimal spectral efficiency in massive multi-user multiple-input multiple-output (MU-MIMO) systems. Such algorithms, however, typically rely on centralized processing at the base station (BS) which results in 1) excessive interconnect and chip input/output (I/O) data rates and 2) high computational complexity. Decentralized baseband processing (DBP) partitions the BS antenna array into independent clusters that are associated with separate radio-frequency circuits and computing fabrics in order to overcome the limitations of centralized processing. In this paper, we investigate decentralized equalization with feedforward architectures that minimize the latency bottlenecks of existing DBP solutions. We propose two distinct architectures with different interconnect and I/O bandwidth requirements that fuse the local equalization results of each cluster in a feedforward network. For both architectures, we consider maximum ratio combining, ZF, L-MMSE, and a nonlinear equalization algorithm that relies on approximate message passing. For these algorithms and architectures, we analyze the associated post-equalization signal-to-noise-and-interference-ratio. We provide reference implementation results on a multigraphics processing unit system which demonstrate that decentralized equalization with feedforward architectures enables throughputs in the Gb/s regime and incurs no or only a small performance loss compared to centralized solutions.

Includes members:
  Member "gaussian message passing for overloaded massive mimo noma":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "code domain noma in massive mimo when is it needed")
  Member "feedforward architectures for decentralized precoding in massive mu mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "design trade offs for decentralized baseband processing in massive mu mimo systems")
  Member "efficient dsp and circuit architectures for massive mimo state of the art and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "jamming suppression in massive mimo systems")
  Member "decentralized baseband processing for massive mu mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cell free massive mimo with radio stripes and sequential uplink processing")
  Member "optimally tuned nonparametric linear equalization for massive mu mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "vlsi design of a nonparametric equalizer for massive mu mimo")
  Member "on the achievable rates of decentralized equalization in massive mu mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "design trade offs for decentralized baseband processing in massive mu mimo systems")
  Member "on the performance of mismatched data detection in large mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal data detection in large mimo")
  Member "performance trade offs in multi processor approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an approximate message passing framework for side information")

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)