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 for feedback capacity of 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, $B^n {\stackrel{\triangle}{=}} \{B_j: j=\ldots, 0,1, \ldots, n\}$ are the channel outputs and $A^n{\stackrel{\triangle}{=}} \{A_j: j=\ldots, 0,1, \ldots, n\}$ are the channel inputs. The characterizations of FTFI capacity, are obtained by first identifying the information structures of the optimal channel input conditional distributions ${\cal P}_{[0,n]} {\stackrel{\triangle}{=}} \big\{ {\bf P}_{A_i|A^{i-1}, B^{i-1}}: i=0, \ldots, n\big\}$, which maximize directed information. The main theorem states, for any channel with memory $M$, the optimal channel input conditional distributions occur in the subset satisfying conditional independence $\stackrel{\circ}{\cal P}_{[0,n]}{\stackrel{\triangle}{=}} \big\{ {\bf P}_{A_i|A^{i-1}, B^{i-1}}= {\bf P}_{A_i|B_{i-M}^{i-1}}: i=1, \ldots, n\big\}$, and the characterization of FTFI capacity is given by $C_{A^n \rightarrow B^n}^{FB, M} {\stackrel{\triangle}{=}} \sup_{ \stackrel{\circ}{\cal P}_{[0,n]} } \sum_{i=0}^n I(A_i; B_i|B_{i-M}^{i-1}) $. The methodology utilizes stochastic optimal control theory 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 ${\cal P}_{[0,n]}$, which are characterized by conditional independence. For any of the above classes of channel distributions and transmission cost functions, a direct analogy, in terms of conditional independence, of the characterizations of FTFI capacity and Shannon's capacity formulae of Memoryless Channels is identified.

Connected through hyperedge HE-1 (induced by source paper "single letter expression of capacity for a class of channels with memory"):
  with members:
    - "information structures of capacity achieving distributions for feedback channels with memory and transmission cost stochastic optimal control variational equalities part i"
    - "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 variational equalities"
    - "capacity and coding for the ising channel with feedback"

Connected through hyperedge HE-2 (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"
    - "information structures of capacity achieving distributions for feedback channels with memory and transmission cost stochastic optimal control variational equalities part i"
    - "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 variational equalities"
    - "directed information on abstract spaces properties and extremum problems"
    - "capacity and coding for the ising channel with feedback"

Connected through hyperedge HE-3 (induced by source paper "directed information on abstract spaces properties and variational equalities"):
  with members:
    - "sequential necessary and sufficient conditions for capacity achieving distributions of channels with memory and feedback"
    - "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"
    - "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)