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: "information structures of capacity achieving distributions for feedback channels with memory and transmission cost stochastic optimal control variational equalities part i"
  Abstract: The Finite Transmission Feedback Information (FTFI) capacity is characterized for any class of channel conditional distributions $\big\{{\bf P}_{B_i|B^{i-1}, A_i} :i=0, 1, \ldots, n\big\}$ and $\big\{ {\bf P}_{B_i|B_{i-M}^{i-1}, A_i} :i=0, 1, \ldots, n\big\}$, where $M$ is the memory of the channel, $B^n = \{B_j: j=0,1, \ldots, n\}$ are the channel outputs and $A^n=\{A_j: j=0,1, \ldots, n\}$ are the channel inputs. The characterizations of FTFI capacity, are obtained by first identifying the information structures of the optimal channel input conditional distributions ${\cal P}_{[0,n]} =\big\{ {\bf P}_{A_i|A^{i-1}, B^{i-1}}: i=1, \ldots, n\big\}$, which maximize directed information $C_{A^n \rightarrow B^n}^{FB} = \sup_{ {\cal P}_{[0,n]} } I(A^n\rightarrow B^n), \ \ \ I(A^n \rightarrow B^n) = \sum_{i=0}^n I(A^i;B_i|B^{i-1}) . $ The main theorem states, for any channel with memory $M$, the optimal channel input conditional distributions occur in the subset ${\cal Q}_{[0,n]}= \big\{ {\bf P}_{A_i|B_{i-M}^{i-1}}: i=1, \ldots, n\big\}$, and the characterization of FTFI capacity is given by $C_{A^n \rightarrow B^n}^{FB, M} = \sup_{ {\cal Q}_{[0,n]} } \sum_{i=0}^n I(A_i; B_i|B_{i-M}^{i-1})$ . Similar conclusions are derived for problems with transmission cost constraints. #R##N#The methodology utilizes stochastic optimal control theory, to identify the control process, the controlled process, and a variational equality of directed information, to derive upper bounds on $I(A^n \rightarrow B^n)$, which are achievable over specific subsets of channel input conditional distributions. For channels with limited memory, this implies the transition probabilities of the channel output process are also of limited memory.

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"
    - "sequential necessary and sufficient conditions for capacity achieving distributions of channels with memory and feedback"
    - "feedback capacity of gaussian channels revisited"
    - "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 "time invariant feedback strategies do not increase capacity of agn channels driven by stable and certain unstable autoregressive noise"):
  with members:
    - "finite state channels with time invariant deterministic feedback"
    - "feedback capacity of stationary gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "single letter expression of capacity for a class of channels with memory"):
  with members:
    - "capacity of binary state symmetric channel with and without feedback and transmission cost"
    - "information structures for feedback capacity of channels with memory and transmission cost stochastic optimal control variational equalities part i"
    - "capacity of a post channel with and without feedback"
    - "directed information on abstract spaces properties and variational equalities"
    - "capacity and coding for the ising channel with feedback"

Connected through hyperedge HE-4 (induced by source paper "sequential necessary and sufficient conditions for capacity achieving distributions of channels with memory and feedback"):
  with members:
    - "capacity achieving distributions information lossless randomized strategies for feedback channels with memory the lqg theory of directed information part ii"
    - "a single letter upper bound on the feedback capacity of unifilar finite state channels"
    - "capacity of binary state symmetric channel with and without feedback and transmission cost"
    - "information structures for feedback capacity of channels with memory and transmission cost stochastic optimal control variational equalities part i"
    - "capacity of a post channel with and without feedback"
    - "directed information on abstract spaces properties and variational equalities"
    - "directed information on abstract spaces properties and extremum problems"
    - "capacity and coding for the ising channel with feedback"

Connected through hyperedge HE-5 (induced by source paper "information structures of extremum problems of feedback capacity for general channels with memory"):
  with members:
    - "capacity of binary state symmetric channel with and without feedback and transmission cost"
    - "capacity of a post channel with and without feedback"
    - "directed information on abstract spaces properties and extremum problems"
    - "a coding theorem for a class of stationary channels with 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-6 (induced by source paper "information structures of maximizing distributions of feedback capacity for general channels with memory applications"):
  with members:
    - "capacity of binary state symmetric channel with and without feedback and transmission cost"
    - "capacity of a post channel with and without feedback"
    - "directed information on abstract spaces properties and extremum problems"
    - "a coding theorem for a class of stationary channels with 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-7 (induced by source paper "capacity achieving distributions information lossless randomized strategies for feedback channels with memory the lqg theory of directed information part ii"):
  with members:
    - "capacity of binary state symmetric channel with and without feedback and transmission cost"
    - "capacity of a post channel with and without feedback"
    - "a coding theorem for a class of stationary channels with feedback"
    - "finite state channels with time invariant deterministic feedback"
    - "feedback capacity of stationary gaussian channels"
    - "the capacity of channels with feedback"

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)