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: "sequential necessary and sufficient conditions for capacity achieving distributions of channels with memory and feedback"
  Abstract: We derive sequential necessary and sufficient conditions for any channel input conditional distribution ${\cal P}_{0,n}\triangleq\{P_{X_t|X^{t-1},Y^{t-1}}:~t=0,\ldots,n\}$ to maximize the finite-time horizon directed information defined by $$C^{FB}_{X^n \rightarrow Y^n} \triangleq \sup_{{\cal P}_{0,n}} I(X^n\rightarrow{Y^n}),~~~ I(X^n \rightarrow Y^n) =\sum_{t=0}^n{I}(X^t;Y_t|Y^{t-1})$$ for channel distributions $\{P_{Y_t|Y^{t-1},X_t}:~t=0,\ldots,n\}$ and $\{P_{Y_t|Y_{t-M}^{t-1},X_t}:~t=0,\ldots,n\}$, where $Y^t\triangleq\{Y_0,\ldots,Y_t\}$ and $X^t\triangleq\{X_0,\ldots,X_t\}$ are the channel input and output random processes, and $M$ is a finite nonnegative integer. #R##N#\noi We apply the necessary and sufficient conditions to application examples of time-varying channels with memory and we derive recursive closed form expressions of the optimal distributions, which maximize the finite-time horizon directed information. Further, we derive the feedback capacity from the asymptotic properties of the optimal distributions by investigating the limit $$C_{X^\infty \rightarrow Y^\infty}^{FB} \triangleq \lim_{n \longrightarrow \infty} \frac{1}{n+1} C_{X^n \rightarrow Y^n}^{FB}$$ without any \'a priori assumptions, such as, stationarity, ergodicity or irreducibility of the channel distribution. The necessary and sufficient conditions can be easily extended to a variety of channels with memory, beyond the ones considered in this paper.

Connected through hyperedge HE-1 (induced by source paper "a connection between feedback capacity and kalman filter for colored gaussian noises"):
  with members:
    - "generic variance bounds on estimation and prediction errors in time series analysis an entropy perspective"
    - "youla coding and computation of gaussian feedback capacity"
    - "feedback capacity of stationary gaussian channels further examined"
    - "feedback capacity of gaussian channels revisited"
    - "information structures of capacity achieving distributions for feedback channels with memory and transmission cost stochastic optimal control variational equalities part i"
    - "capacity achieving feedback schemes for gaussian finite state markov channels with channel state information"
    - "convergence of fundamental limitations in feedback communication estimation and feedback control over gaussian channels"
    - "on the feedback capacity of power constrained gaussian noise channels with memory"

Connected through hyperedge HE-2 (induced by source paper "information structures for feedback capacity of channels with memory and transmission cost stochastic optimal control variational equalities part i"):
  with members:
    - "capacity achieving distributions information lossless randomized strategies for feedback channels with memory the lqg theory of directed information part ii"
    - "capacity of binary state symmetric channel with and without feedback and transmission cost"
    - "capacity of a post channel with and without feedback"
    - "finite state channels with time invariant deterministic feedback"
    - "feedback capacity of stationary gaussian channels"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-3 (induced by source paper "directed information on abstract spaces properties and variational equalities"):
  with members:
    - "capacity achieving distributions information lossless randomized strategies for feedback channels with memory the lqg theory of directed information part ii"
    - "optimal nonanticipative estimation schemes for time varying gauss markov processes"
    - "capacity of binary state symmetric channel with and without feedback and transmission cost"
    - "information nonanticipative rate distortion function and its applications"
    - "information structures for feedback capacity of channels with memory and transmission cost stochastic optimal control variational equalities part i"
    - "nonanticipative rate distortion function and relations to filtering theory"
    - "capacity of a post channel with and without feedback"
    - "optimal nonstationary reproduction distribution for nonanticipative rdf on abstract alphabets"

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)