human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources"
  Abstract: We improve the existing achievable rate regions for causal and for zero-delay source coding of stationary Gaussian sources under an average mean squared error (MSE) distortion measure. To begin with, we find a closed-form expression for the information-theoretic causal rate-distortion function (RDF) under such distortion measure, denoted by $R_{c}^{it}(D)$, for first-order Gauss-Markov processes. Rc^{it}(D) is a lower bound to the optimal performance theoretically attainable (OPTA) by any causal source code, namely Rc^{op}(D). We show that, for Gaussian sources, the latter can also be upper bounded as Rc^{op}(D)\leq Rc^{it}(D) + 0.5 log_{2}(2\pi e) bits/sample. In order to analyze $R_{c}^{it}(D)$ for arbitrary zero-mean Gaussian stationary sources, we introduce \bar{Rc^{it}}(D), the information-theoretic causal RDF when the reconstruction error is jointly stationary with the source. Based upon \bar{Rc^{it}}(D), we derive three closed-form upper bounds to the additive rate loss defined as \bar{Rc^{it}}(D) - R(D), where R(D) denotes Shannon's RDF. Two of these bounds are strictly smaller than 0.5 bits/sample at all rates. These bounds differ from one another in their tightness and ease of evaluation; the tighter the bound, the more involved its evaluation. We then show that, for any source spectral density and any positive distortion D\leq \sigma_{x}^{2}, \bar{Rc^{it}}(D) can be realized by an AWGN channel surrounded by a unique set of causal pre-, post-, and feedback filters. We show that finding such filters constitutes a convex optimization problem. In order to solve the latter, we propose an iterative optimization procedure that yields the optimal filters and is guaranteed to converge to \bar{Rc^{it}}(D). Finally, by establishing a connection to feedback quantization we design a causal and a zero-delay coding scheme which, for Gaussian sources, achieves...

Connected through hyperedge HE-1 (induced by source paper "a converse bound characterization on zero delay indirect rdf for vector gauss markov sources"):
  with members:
    - "zero delay rate distortion via filtering for vector valued gaussian sources"
    - "optimal estimation via nonanticipative rate distortion function and applications to time varying gauss markov processes"
    - "directed information on abstract spaces properties and variational equalities"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-2 (induced by source paper "optimal causal rate constrained sampling of the wiener process"):
  with members:
    - "remote estimation of the wiener process over a channel with random delay"
    - "event triggered stabilization of linear systems under bounded bit rates"
    - "infinite horizon optimal transmission power control for remote state estimation over fading channels"
    - "the distortion rate function of sampled wiener processes"
    - "rate cost tradeoffs in control"
    - "optimal estimation via nonanticipative rate distortion function and applications to time varying gauss markov processes"
    - "event triggered control under time varying rates and channel blackouts"
    - "optimal estimation with limited measurements and noisy communication"

Connected through hyperedge HE-3 (induced by source paper "on zero delay rdf for vector valued gauss markov sources with additional noise"):
  with members:
    - "zero delay rate distortion via filtering for vector valued gaussian sources"
    - "optimal estimation via nonanticipative rate distortion function and applications to time varying gauss markov processes"
    - "indirect rate distortion function of a binary i i d source"
    - "directed information on abstract spaces properties and variational equalities"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-4 (induced by source paper "fundamental limits of distributed tracking"):
  with members:
    - "rate cost tradeoffs in control"
    - "a characterization of the minimal average data rate that guarantees a given closed loop performance level"
    - "multiterminal source coding under logarithmic loss"
    - "successive wyner ziv coding scheme and its application to the quadratic gaussian ceo problem"
    - "rate region of the quadratic gaussian two encoder source coding problem"

Connected through hyperedge HE-5 (induced by source paper "zero delay rate distortion via filtering for vector valued gaussian sources"):
  with members:
    - "an upper bound to zero delay rate distortion via kalman filtering for vector gaussian sources"
    - "filtering with fidelity for time varying gauss markov processes"
    - "a characterization of the minimal average data rate that guarantees a given closed loop performance level"
    - "nonanticipative rate distortion function and relations to filtering theory"
    - "directed information on abstract spaces properties and variational equalities"
    - "achieving the gaussian rate distortion function by prediction"

Connected through hyperedge HE-6 (induced by source paper "stationarity in the realizations of the causal rate distortion function for one sided stationary sources"):
  with members:
    - "an upper bound to zero delay rate distortion via kalman filtering for vector gaussian sources"
    - "a characterization of the minimal average data rate that guarantees a given closed loop performance level"
    - "information nonanticipative rate distortion function and its applications"
    - "feedback capacity of stationary gaussian channels"

Connected through hyperedge HE-7 (induced by source paper "an upper bound to zero delay rate distortion via kalman filtering for vector gaussian sources"):
  with members:
    - "filtering with fidelity for time varying gauss markov processes"
    - "directed information on abstract spaces properties and variational equalities"

Connected through hyperedge HE-8 (induced by source paper "zero delay rate distortion via filtering for unstable vector gaussian sources"):
  with members:
    - "directed information on abstract spaces properties and variational equalities"

Question: Please tell me which class the center node 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)