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: "a single letter upper bound on the feedback capacity of unifilar finite state channels"
  Abstract: An upper bound on the feedback capacity of unifilar finite-state channels (FSCs) is derived. A new technique, called the $Q$-contexts, is based on a construction of a directed graph that is used to quantize recursively the receiver's output sequences to a finite set of contexts. For any choice of $Q$-graph, the feedback capacity is bounded by a single-letter expression, $C_\text{fb}\leq \sup I(X,S;Y|Q)$, where the supremum is over $P_{X|S,Q}$ and the distribution of $(S,Q)$ is their stationary distribution. It is shown that the bound is tight for all unifilar FSCs where feedback capacity is known: channels where the state is a function of the outputs, the trapdoor channel, Ising channels, the no-consecutive-ones input-constrained erasure channel and for the memoryless channel. Its efficiency is also demonstrated by deriving a new capacity result for the dicode erasure channel (DEC); the upper bound is obtained directly from the above expression and its tightness is concluded with a general sufficient condition on the optimality of the upper bound. This sufficient condition is based on a fixed point principle of the BCJR equation and, indeed, formulated as a simple lower bound on feedback capacity of unifilar FSCs for arbitrary $Q$-graphs. This upper bound indicates that a single-letter expression might exist for the capacity of finite-state channels with or without feedback based on a construction of auxiliary random variable with specified structure, such as $Q$-graph, and not with i.i.d distribution. The upper bound also serves as a non-trivial bound on the capacity of channels without feedback, a problem that is still open.

Connected through hyperedge HE-1 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning"):
  with members:
    - "deepcode feedback codes via deep learning"
    - "feedback capacity and coding for the bibo channel with a no repeated ones input constraint"
    - "continuous control with deep reinforcement learning"
    - "capacity of a post channel with and without feedback"
    - "capacity and coding for the ising channel with feedback"
    - "finite state channels with time invariant deterministic feedback"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-2 (induced by source paper "computable lower bounds for capacities of input driven finite state channels"):
  with members:
    - "feedback capacity and coding for the bibo channel with a no repeated ones input constraint"
    - "asymptotics of input constrained erasure channel capacity"
    - "dual capacity upper bounds for noisy runlength constrained channels"
    - "novel lower bounds on the entropy rate of binary hidden markov processes"

Connected through hyperedge HE-3 (induced by source paper "graph based encoders and their performance for finite state channels with feedback"):
  with members:
    - "feedback capacity and coding for the 0 k rll input constrained bec"
    - "feedback capacity and coding for the bibo channel with a no repeated ones input constraint"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "capacity and coding for the ising channel with feedback"
    - "write channel model for bit patterned media recording"
    - "optimal feedback communication via posterior matching"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-4 (induced by source paper "feedback capacity and coding for the bibo channel with a no repeated ones input constraint"):
  with members:
    - "asymptotics of input constrained erasure channel capacity"
    - "dual capacity upper bounds for noisy runlength constrained channels"
    - "an efficient feedback coding scheme with low error probability for discrete memoryless channels"
    - "a simple proof for the optimality of randomized posterior matching"
    - "can feedback increase the capacity of the energy harvesting channel"
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "capacity and coding for the ising channel with feedback"
    - "optimal feedback communication via posterior matching"

Connected through hyperedge HE-5 (induced by source paper "feedback capacity and coding for the 0 k rll input constrained bec"):
  with members:
    - "feedback capacity and coding for the bibo channel with a no repeated ones input constraint"
    - "an efficient feedback coding scheme with low error probability for discrete memoryless channels"
    - "a simple proof for the optimality of randomized posterior matching"
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "modulation coding for flash memories"
    - "capacity and coding for the ising channel with feedback"
    - "optimal feedback communication via posterior matching"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-6 (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"
    - "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"
    - "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"

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)