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 "estimating the directed information and testing for causality"):
  Abstract: The problem of estimating the directed information rate between two discrete processes $\{X_n\}$ and $\{Y_n\}$ via the plug-in (or maximum-likelihood) estimator is considered. When the joint process $\{(X_n,Y_n)\}$ is a Markov chain of a given memory length, the plug-in estimator is shown to be asymptotically Gaussian and to converge at the optimal rate $O(1/\sqrt{n})$ under appropriate conditions; this is the first estimator that has been shown to achieve this rate. An important connection is drawn between the problem of estimating the directed information rate and that of performing a hypothesis test for the presence of causal influence between the two processes. Under fairly general conditions, the null hypothesis, which corresponds to the absence of causal influence, is equivalent to the requirement that the directed information rate be equal to zero. In that case a finer result is established, showing that the plug-in converges at the faster rate $O(1/n)$ and that it is asymptotically $\chi^2$-distributed. This is proved by showing that this estimator is equal to (a scalar multiple of) the classical likelihood ratio statistic for the above hypothesis test. Finally it is noted that these results facilitate the design of an actual likelihood ratio test for the presence or absence of causal influence.

Includes members:
  Member "a characterization of the minimal average data rate that guarantees a given closed loop performance level":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fundamental limits of distributed tracking")
  Member "information networks with in block memory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a unified inner bound for the two receiver memoryless broadcast channel with channel state and message side information")
  Member "universal estimation of directed information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity of continuous channels with memory via directed information neural estimator")
  Member "detection and isolation of link failures under the agreement protocol":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "detection and isolation of failures in linear multi agent networks")
  Member "the relation between granger causality and directed information theory a review":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "measuring sample path causal influences with relative entropy")
  Member "directed information causal estimation and communication in continuous time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generic variance bounds on estimation and prediction errors in time series analysis an entropy perspective")
  Member "interpretations of directed information in portfolio theory data compression and hypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity of continuous channels with memory via directed information neural estimator")
  Member "feedback capacity of the compound channel":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the reliability function of the common message broadcast channel with variable length feedback")

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)