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 "generalized bayesian cramer rao inequality via information geometry of relative alpha entropy"):
  Abstract: The relative $\alpha$-entropy is the Renyi analog of relative entropy and arises prominently in information-theoretic problems. Recent information geometric investigations on this quantity have enabled the generalization of the Cramer-Rao inequality, which provides a lower bound for the variance of an estimator of an escort of the underlying parametric probability distribution. However, this framework remains unexamined in the Bayesian framework. In this paper, we propose a general Riemannian metric based on relative $\alpha$-entropy to obtain a generalized Bayesian Cramer-Rao inequality. This establishes a lower bound for the variance of an unbiased estimator for the $\alpha$-escort distribution starting from an unbiased estimator for the underlying distribution. We show that in the limiting case when the entropy order approaches unity, this framework reduces to the conventional Bayesian Cramer-Rao inequality. Further, in the absence of priors, the same framework yields the deterministic Cramer-Rao inequality.

Includes members:
  Member "cramer rao lower bounds arising from generalized csiszar divergences":
    also connected through other hyperedges:
      (none)
  Member "information geometric approach to bayesian lower error bounds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cramer rao lower bounds arising from generalized csiszar divergences")
  Member "performance of time delay estimation in a cognitive radar":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dictionary learning for adaptive gpr landmine classification")
  Member "minimization problems based on relative alpha entropy ii reverse projection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cramer rao lower bounds arising from generalized csiszar divergences")
  Member "minimization problems based on relative alpha entropy i forward projection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cramer rao lower bounds arising from generalized csiszar divergences")
  Member "guessing under source uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cramer rao lower bounds arising from generalized csiszar divergences")

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)