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: "training optimization for gauss markov rayleigh fading channels"
  Abstract: In this paper, pilot-assisted transmission over Gauss-Markov Rayleigh fading channels is considered. A simple scenario, where a single pilot signal is transmitted every T symbols and T - 1 data symbols are transmitted in between the pilots, is studied. First, it is assumed that binary phase-shift keying (BPSK) modulation is employed at the transmitter. With this assumption, the training period, and data and training power allocation are jointly optimized by maximizing an achievable rate expression. Achievable rates and energy-per-bit requirements are computed using the optimal training parameters. Secondly, a capacity lower bound is obtained by considering the error in the estimate as another source of additive Gaussian noise, and the training parameters are optimized by maximizing this lower bound.

Connected through hyperedge HE-1 (induced by source paper "optimal training for non feedback adaptive psam over time varying rayleigh fading channels"):
  with members:
    - "fundamental limits of cooperation"
    - "adaptive training for correlated fading channels with feedback"

Connected through hyperedge HE-2 (induced by source paper "ergodic capacity analysis in cognitive radio systems under channel uncertainty"):
  with members:
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"
    - "wideband spectrum sensing in cognitive radio networks"
    - "pilot symbol assisted communications with noncausal and causal wiener filters"

Connected through hyperedge HE-3 (induced by source paper "on the capacity and energy efficiency of training based transmissions over fading channels"):
  with members:
    - "pilot symbol assisted communications with noncausal and causal wiener filters"
    - "capacity of sparse multipath channels in the ultra wideband regime"
    - "the noncoherent rician fading channel part i structure of the capacity achieving input"
    - "noncoherent rician fading channel part ii spectral efficiency in the low power regime"
    - "channel uncertainty in ultra wideband communication systems"
    - "capacity per unit energy of fading channels with a peak constraint"

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)