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 "stationarity in the realizations of the causal rate distortion function for one sided stationary sources"):
  Abstract: This paper derives novel results on the characterization of the the causal information rate-distortion function (IRDF) $R_{c}^{it}(D)$ for arbitrarily-distributed one-sided stationary $\kappa$-th order Markov source x(1),x(2),.... It is first shown that Gorbunov and Pinsker's results on the stationarity of the realizations to the causal IRDF (stated for two-sided stationary sources) do not apply to the commonly used family of asymptotic average single-letter (AASL) distortion criteria. Moreover, we show that, in general, a reconstruction sequence cannot be both jointly stationary with a one-sided stationary source sequence and causally related to it. This implies that, in general, the causal IRDF for one-sided stationary sources cannot be realized by a stationary distribution. However, we prove that for an arbitrarily distributed one-sided stationary source and a large class of distortion criteria (including AASL), the search for $R_{c}^{it}(D)$ can be restricted to distributions which yield the output sequence y(1), y(2),... jointly stationary with the source after $\kappa$ samples. Finally, we improve the definition of the stationary causal IRDF $\overline{R}_{c}^{it}(D)$ previously introduced by Derpich and {\O}stergaard for two-sided Markovian stationary sources and show that $\overline{R}_{c}^{it}(D)$ for a two-sided source ...,x(-1),x(0),x(1),... equals $R_{c}^{it}(D)$ for the associated one-sided source x(1), x(2),.... This implies that, for the Gaussian quadratic case, the practical zero-delay encoder-decoder pairs proposed by Derpich and {\O}stergaard for approaching $R_{c}^{it}(D)$ achieve an operational data rate which exceeds $R_{c}^{it}(D)$ by less than $1+0.5 \log_2(2 \pi e /12) \simeq 1.254$ bits per sample.

Includes members:
  Member "an upper bound to zero delay rate distortion via kalman filtering for vector gaussian sources":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "zero delay rate distortion via filtering for vector valued gaussian sources")
  Member "a characterization of the minimal average data rate that guarantees a given closed loop performance level":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fundamental limits of distributed tracking")
  Member "information nonanticipative rate distortion function and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transfer entropy in mdps with temporal logic specifications")
  Member "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a converse bound characterization on zero delay indirect rdf for vector gauss markov sources")
  Member "feedback capacity of stationary gaussian channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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)