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 "a generalized expression for the gradient of the fundamental information measures"):
  Abstract: This paper introduces a general expression for the gradient of some fundamental information measures. We first calculate the variation of mutual information caused by a small variation in its arguments. Then, the notion of the gradient of mutual information with respect to the system parameters is derived in general form without imposing any constraints on the system model or even on the characteristics of the noise process. In the introduced notion of the gradient of mutual information, the system input can be dependent on the system parameters, which can happen in many realistic scenarios such as communication channels with feedback. The key feature of this expression is that, it models the effects of the system parameters on the mutual information as the superposition of the two distinct effects. The first one is the effect of the system parameters on the mutual information through the system input and the second one is the effect of the system parameters on the mutual information through the system output. Moreover, as special versions of our general results, description of the gradient of mutual information with respect to the several quantities of interest are given in additive noise channels without imposing any constraints on the noise distribution. Also, gradient of the differential entropy and relative entropy, as two other basic measures in information theory, with respect to the channel and noise parameters, are explored in additive noise channels. It is shown that, our general results will reduce to their Gaussian counterparts, presented in the literature.

Includes members:
  Member "information estimation and lookahead in the gaussian channel":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a generalized expression for the gradient of mutual information application in multiple access channel with feedback")
  Member "extensions of the i mmse relation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capacity regions of families of continuous time multi user gaussian channels")
  Member "pointwise relations between information and estimation in gaussian noise":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a generalized expression for the gradient of mutual information with the application in multiple access channels")
  Member "mutual information relative entropy and estimation in the poisson channel":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a class of lower bounds for bayesian risk with a bregman loss")
  Member "gaussian interference channel capacity to within one bit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fundamental limits of wireless caching under mixed cacheable and uncacheable traffic")
  Member "mutual information and minimum mean square error in gaussian channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")

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)