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: "optimization of information rate upper and lower bounds for channels with memory"
  Abstract: We consider the problem of minimizing upper bounds and maximizing lower bounds on information rates of stationary and ergodic discrete-time channels with memory. The channels we consider can have a finite number of states, such as partial response channels, or they can have an infinite state space, such as time-varying fading channels. We optimize recently proposed information rate bounds for such channels, which make use of auxiliary finite-state machine channels (FSMCs). Our main contribution in this paper is to provide iterative expectation-maximization (EM) type algorithms to optimize the parameters of the auxiliary FSMC to tighten these bounds. We provide an explicit, iterative algorithm that improves the upper bound at each iteration. We also provide an effective method for iteratively optimizing the lower bound. To demonstrate the effectiveness of our algorithms, we provide several examples of partial response and fading channels where the proposed optimization techniques significantly tighten the initial upper and lower bounds. Finally, we compare our results with results obtained by the conjugate gradient optimization algorithm and an improved variation of the simplex algorithm, called Soblex. While the computational complexities of our algorithms are similar to the conjugate gradient method and less than the Soblex algorithm, our algorithms robustly find the tightest bounds. Interestingly, from a channel coding/decoding perspective, optimizing the lower bound is related to increasing the achievable mismatched information rate, i.e., the information rate of a communication system where the decoder at the receiver is matched to the auxiliary channel, and not to the original channel.

Connected through hyperedge HE-1 (induced by source paper "bounding and estimating the classical information rate of quantum channels with memory"):
  with members:
    - "double edge factor graphs definition properties and examples"
    - "factor graphs for quantum probabilities"

Connected through hyperedge HE-2 (induced by source paper "improved lower bounds on mutual information accounting for nonlinear signal noise interaction"):
  with members:
    - "information rates of next generation long haul optical fiber systems using coded modulation"
    - "impact of 4d channel distribution on the achievable rates in coherent optical communication experiments"
    - "stochastic digital backpropagation with residual memory compensation"
    - "upper bound on the capacity of a cascade of nonlinear and noisy channels"
    - "on achievable rates for long haul fiber optic communications"
    - "capacity of a nonlinear optical channel with finite memory"

Connected through hyperedge HE-3 (induced by source paper "gaussian intersymbol interference channels with mismatch"):
  with members:
    - "on the asymptotic equivalence of circulant and toeplitz matrices"
    - "on the evaluation of the polyanskiy poor verdu converse bound for finite block length coding in awgn"
    - "random coding error exponents for the two user interference channel"
    - "universal decoding for gaussian intersymbol interference channels"
    - "erasure list exponents for slepian wolf decoding"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "a general formula for the mismatch capacity"
    - "analysis of mismatched estimation errors using gradients of partition functions"

Connected through hyperedge HE-4 (induced by source paper "tighter lower bounds on mutual information for fiber optic channels"):
  with members:
    - "impact of 4d channel distribution on the achievable rates in coherent optical communication experiments"
    - "stochastic digital backpropagation with residual memory compensation"
    - "upper bound on the capacity of a cascade of nonlinear and noisy channels"
    - "on achievable rates for long haul fiber optic communications"

Connected through hyperedge HE-5 (induced by source paper "lower bounds and approximations for the information rate of the isi channel"):
  with members:
    - "comparison of the achievable rates in ofdm and single carrier modulation with i i d inputs"
    - "easily computed lower bounds on the information rate of intersymbol interference channels"
    - "estimation in gaussian noise properties of the minimum mean square error"
    - "a coordinate system for gaussian networks"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-6 (induced by source paper "stationary and transition probabilities in slow mixing long memory markov processes"):
  with members:
    - "compression based methods for nonparametric density estimation on line prediction regression and classification for time series"

Connected through hyperedge HE-7 (induced by source paper "upper bounds on the capacities of noncontrollable finite state channels with without feedback"):
  with members:
    - "on the feedback capacity of power constrained gaussian noise channels with memory"
    - "a coding theorem for a class of stationary channels with feedback"
    - "finite state channels with time invariant deterministic feedback"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-8 (induced by source paper "the capacity region of the degraded finite state broadcast channel"):
  with members:
    - "finite state channels with time invariant deterministic feedback"
    - "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)