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 "modulus zero forcing detection for mimo channels"):
  Abstract: We propose a modulus based zero-forcing (MZF) detection for multi-input multi-output (MIMO) channels. Traditionally, a ZF detector nulls out all interferences from other layers when detecting a current layer, which can yield suboptimal detection-performance due to the noise-enhancement issue. In many communication systems, finite alphabets such as M quadrature-amplitude-modulation (QAM) are widely used, which comprises \sqrt{M} pulse-amplitude-modulation (PAM) symbols for the real and imaginary parts. With finite alphabets, one feasible way to improve ZF detection is to allow controllable interferences that can be removed away by modulus operations.

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 "compute and forward strategies for cooperative distributed antenna systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "private information retrieval over gaussian mac")
  Member "faster recursions in sphere decoding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to detect")
  Member "compute and forward harnessing interference through structured codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "private information retrieval over gaussian mac")
  Member "communication over mimo broadcast channels using lattice basis reduction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on low complexity lattice reduction algorithms for large scale mimo detection the blessing of sequential reduction")
  Member "lll reduction achieves the receive diversity in mimo decoding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on low complexity lattice reduction algorithms for large scale mimo detection the blessing of sequential reduction")

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)