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 "lqg control approach to gaussian broadcast channels with feedback"):
  Abstract: A code for communication over the k-receiver additive white Gaussian noise broadcast channel with feedback is presented and analyzed using tools from the theory of linear quadratic Gaussian optimal control. It is shown that the performance of this code depends on the noise correlation at the receivers and it is related to the solution of a discrete algebraic Riccati equation. For the case of independent noises, the sum rate achieved by the proposed code, satisfying average power constraint P, is characterized as 1/2 log (1+P*phi), where the coefficient "phi" in the interval [1,k] quantifies the power gain due to the presence of feedback. When specialized to the case of two receivers, this includes a previous result by Elia and strictly improves upon the code of Ozarow and Leung. When the noises are correlated, the pre-log of the sum-capacity of the broadcast channel with feedback can be strictly greater than one. It is established that for all noise covariance matrices of rank r the pre-log of the sum capacity is at most k-r+1 and, conversely, there exists a noise covariance matrix of rank r for which the proposed code achieves this upper bound. This generalizes a previous result by Gastpar and Wigger for the two-receiver broadcast channel.

Includes members:
  Member "an achievable rate region for the broadcast channel with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploiting location information to enhance throughput in downlink v2i systems")
  Member "control theoretic approach to communication with feedback fundamental limits and code design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "information theoretic viewpoints on optimal causal coding decoding problems")
  Member "on the capacity of the discrete memoryless broadcast channel with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploiting location information to enhance throughput in downlink v2i systems")
  Member "lecture notes on network information theory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the required number of antennas in a point to point large but finite mimo system outage limited scenario")
  Member "linear sum capacity for gaussian multiple access channels with feedback":
    also connected through other hyperedges:
      (none)
  Member "optimal feedback communication via posterior matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "feedback channel communication with low precision arithmetic")
  Member "the pre log of gaussian broadcast with feedback can be two":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "posterior matching for gaussian broadcast channels with feedback")
  Member "feedback capacity of stationary gaussian channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity of continuous channels with memory via directed information neural estimator")

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)