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: "universal distributed estimation over multiple access channels with constant modulus signaling"
  Abstract: A distributed estimation scheme where the sensors transmit with constant modulus signals over a multiple access channel is considered. The proposed estimator is shown to be strongly consistent for any sensing noise distribution in the i.i.d. case both for a per-sensor power constraint, and a total power constraint. When the distributions of the sensing noise are not identical, a bound on their variances is shown to establish strong consistency. The estimator is shown to be asymptotically normal with a variance ( AsV) that depends on the characteristic function of the sensing noise. Optimization of the AsV is considered with respect to a transmission phase parameter for a variety of noise distributions exhibiting differing levels of impulsive behavior. The robustness of the estimator in the sense of degrading gracefully when the sensing noise deviates from the Gaussian distribution to impulsive sensing noise distributions such as those with positive excess kurtosis, or those that do not have finite moments is shown. The proposed estimator is favorably compared with the amplify and forward scheme under an impulsive noise scenario. The effect of fading and phase error at the sensors are shown to not affect the consistency of the estimator, but to degrade the asymptotic variance by terms that depend on the fading and phase error distributions. Simulations corroborate our analytical results.

Connected through hyperedge HE-1 (induced by source paper "distributed estimation and detection with bounded transmissions over gaussian multiple access channels"):
  with members:
    - "non linear distributed average consensus using bounded transmissions"
    - "robust analog function computation via wireless multiple access channels"
    - "distributed snr estimation using constant modulus signaling over gaussian multiple access channels"
    - "distributed detection over gaussian multiple access channels with constant modulus signaling"
    - "estimation diversity and energy efficiency in distributed sensing"

Connected through hyperedge HE-2 (induced by source paper "estimation in phase shift and forward wireless sensor networks"):
  with members:
    - "phase only analog encoding for a multi antenna fusion center"
    - "the ergodic capacity of phase fading interference networks"
    - "on scaling laws of diversity schemes in decentralized estimation"
    - "distributed detection over fading macs with multiple antennas at the fusion center"
    - "optimal spectrum sharing in mimo cognitive radio networks via semidefinite programming"
    - "estimation diversity and energy efficiency in distributed sensing"

Connected through hyperedge HE-3 (induced by source paper "distributed snr estimation using constant modulus signaling over gaussian multiple access channels"):
  with members:
    - "on inequalities relating the characteristic function and fisher information"

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)