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 "time invariant feedback strategies do not increase capacity of agn channels driven by stable and certain unstable autoregressive noise"):
  Abstract: The capacity of additive Gaussian noise (AGN) channels with feedback, when the noise is described by, stable and unstable, autoregressive models, is characterized, under the condition that, channel input feedback strategies are time-invariant and induce asymptotic stationarity, and ergodicity of the channel output process. This condition is more relaxed than imposing stationarity or asymptotic stationarity of the joint input and output process, as done in [1]. Based on this characterization, new closed form capacity formulas, and lower bounds are derived, for the stable and unstable AR$(c)$ noise, $V_t=cV_{t-1}+ W_t, V_0=v_0, t=1, \ldots, n$, where $c\in (-\infty,\infty)$, $W_t, t=1,\ldots, n$, is a zero mean, variance $K_W$, independent Gaussian sequence, independent of $V_0$. Our capacity formulas are fundamentally different from existing formulas found in the literature. The new formulas illustrate multiple regimes of capacity, as a function of the parameters $(c,K_W,\kappa)$, where $\kappa$ is the total average power allocated to the transmitter. In particular, 1) feedback increases capacity for the regime, $c^2 \in (1, \infty),$ for $\kappa > \frac{K_W}{\big(c^2-1\big)^2}$, 2) feedback does not increase capacity for the regime $c^2 \in (1, \infty)$, for $\kappa \leq \frac{K_W}{\big(c^2-1\big)^2}$, and 3) feedback does not increase capacity for the regime $c \in [-1,1]$, for $ \kappa \in [0,\infty)$. We show that our disagreement with [1] is mainly attributed to the use of necessary and sufficient conditions for convergence of generalized difference Riccati equations (DREs) to analogous generalized algebraic Riccati equations (AREs), of Gaussian estimation problems, known as detectability and stabilizability conditions, to ensure asymptotic stationarity and ergodicity of the channel output process.

Includes members:
  Member "information structures of capacity achieving distributions for feedback channels with memory and transmission cost stochastic optimal control variational equalities part i":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a connection between feedback capacity and kalman filter for colored gaussian noises")
  Member "finite state channels with time invariant deterministic feedback":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning")
  Member "feedback capacity of stationary gaussian channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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)