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 "weak noise modulation estimation of vector parameters"):
  Abstract: We address the problem of modulating a parameter onto a power-limited signal, transmitted over a discrete-time Gaussian channel and estimating this parameter at the receiver. Continuing an earlier work, where the optimal trade-off between the weak-noise estimation performance and the outage probability (threshold-effect breakdown) was studied for a single (scalar) parameter, here we extend the derivation of the weak-noise estimation performance to the case of a multi-dimensional vector parameter. This turns out to be a non-trivial extension, that provides a few insights and it has some interesting implications, which are discussed in depth. Several modifications and extensions of the basic setup are also studied and discussed.

Includes members:
  Member "trade offs between weak noise estimation performance and outage exponents in nonlinear modulation":
    also connected through other hyperedges:
      (none)
  Member "converse bounds on modulation estimation performance for the gaussian multiple access channel":
    also connected through other hyperedges:
      (none)
  Member "exponential error bounds on parameter modulation estimation for discrete memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the minimum mean p th error in gaussian noise channels and its applications")
  Member "on optimum parameter modulation estimation from a large deviations perspective":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lower bounds on parameter modulation estimation under bandwidth constraints")

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)