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 "a simple capacity lower bound for communication with superimposed pilots"):
  Abstract: We present a novel closed-form lower bound on the Gaussian-input mutual information for noncoherent communication (i.e., in which neither transmitter nor receiver are cognizant of the fading state) over a frequency-flat fading channel with additive noise. Our bound yields positive (non-trivial) values even in the most challenging case of zero-mean fast fading, a regime in which the conventional approach of orthogonal time-multiplexed pilots is unavailing and for which, to the best of the author's knowledge, no simple analytical bound is known. Its derivation relies on endowing the transmit signal with a non-zero mean, which can be interpreted as a pilot symbol that is additively superimposed onto the information-bearing Gaussian signal. The optimal fraction of transmit power that one should dedicate to this pilot is computed in closed form and shown to tend to one half at low SNR and to a limit above $2-\sqrt{2} \approx 0.586$ at high SNR. We further show how one can refine the bound for the general case of non-zero mean fading. Finally, we state an extension of our bound to the MIMO setting and apply it to compare superimposed vs. orthogonal pilots on the SISO Rayleigh block fading channel.

Includes members:
  Member "superimposed pilots are superior for mitigating pilot contamination in massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint frame design and resource allocation for ultra reliable and low latency vehicular networks")
  Member "a rate splitting approach to fading channels with imperfect channel state information":
    also connected through other hyperedges:
      (none)
  Member "nearest neighbour decoding and pilot aided channel estimation in stationary gaussian flat fading channels":
    also connected through other hyperedges:
      (none)
  Member "what is the value of joint processing of pilots and data in block fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a data aided channel estimation scheme for decoupled systems in heterogeneous networks")
  Member "the noncoherent rician fading channel part i structure of the capacity achieving input":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity achieving input distributions in nondispersive optical fibers")

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)