human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "power allocation for discrete input delay limited fading channels"
  Abstract: We consider power allocation algorithms for fixed-rate transmission over Nakagami-m non-ergodic block-fading channels with perfect transmitter and receiver channel state information and discrete input signal constellations, under both short- and long-term power constraints. Optimal power allocation schemes are shown to be direct applications of previous results in the literature. We show that the SNR exponent of the optimal short-term scheme is given by m times the Singleton bound. We also illustrate the significant gains available by employing long-term power constraints. In particular, we analyze the optimal long-term solution, showing that zero outage can be achieved provided that the corresponding short-term SNR exponent with the same system parameters is strictly greater than one. Conversely, if the short-term SNR exponent is smaller than one, we show that zero outage cannot be achieved. In this case, we derive the corresponding long-term SNR exponent as a function of the Singleton bound. Due to the nature of the expressions involved, the complexity of optimal schemes may be prohibitive for system implementation. We therefore propose simple sub-optimal power allocation schemes whose outage probability performance is very close to the minimum outage probability obtained by optimal schemes. We also show the applicability of these techniques to practical systems employing orthogonal frequency division multiplexing.

Connected through hyperedge HE-1 (induced by source paper "power allocation for fading channels with peak to average power constraints"):
  with members:
    - "derivative of bicm mutual information"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-2 (induced by source paper "outage probability of the gaussian mimo free space optical channel with ppm"):
  with members:
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "derivative of bicm mutual information"):
  with members:
    - "mutual information and minimum mean square error in gaussian channels"

Question: Please tell me which class the center node 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)