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: "feedback capacity of the first order moving average gaussian channel"
  Abstract: The feedback capacity of the stationary Gaussian additive noise channel has been open, except for the case where the noise is white. Here we find the feedback capacity of the stationary first-order moving average additive Gaussian noise channel in closed form. Specifically, the channel is given by $Y_i = X_i + Z_i,$ $i = 1, 2, ...,$ where the input $\{X_i\}$ satisfies a power constraint and the noise $\{Z_i\}$ is a first-order moving average Gaussian process defined by $Z_i = \alpha U_{i-1} + U_i,$ $|\alpha| \le 1,$ with white Gaussian innovations $U_i,$ $i = 0,1,....$ #R##N#We show that the feedback capacity of this channel is $-\log x_0,$ where $x_0$ is the unique positive root of the equation $ \rho x^2 = (1-x^2) (1 - |\alpha|x)^2,$ and $\rho$ is the ratio of the average input power per transmission to the variance of the noise innovation $U_i$. The optimal coding scheme parallels the simple linear signalling scheme by Schalkwijk and Kailath for the additive white Gaussian noise channel -- the transmitter sends a real-valued information-bearing signal at the beginning of communication and subsequently refines the receiver's error by processing the feedback noise signal through a linear stationary first-order autoregressive filter. The resulting error probability of the maximum likelihood decoding decays doubly-exponentially in the duration of the communication. This feedback capacity of the first-order moving average Gaussian channel is very similar in form to the best known achievable rate for the first-order \emph{autoregressive} Gaussian noise channel studied by Butman, Wolfowitz, and Tiernan, although the optimality of the latter is yet to be established.

Connected through hyperedge HE-1 (induced by source paper "capacity of continuous channels with memory via directed information neural estimator"):
  with members:
    - "computing the feedback capacity of finite state channels using reinforcement learning"
    - "conditional mutual information neural estimator"
    - "deep learning for channel coding via neural mutual information estimation"
    - "neural entropic estimation a faster path to mutual information estimation"
    - "itene intrinsic transfer entropy neural estimator"
    - "mine mutual information neural estimation"
    - "estimating the directed information and testing for causality"
    - "universal estimation of directed information"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "two way function computation"):
  with members:
    - "two way interference channel capacity how to have the cake and eat it too"
    - "feedback through overhearing"
    - "capacity of a post channel with and without feedback"
    - "perfect output feedback in the two user decentralized interference channel"
    - "computation in multicast networks function alignment and converse theorems"
    - "two way interference channels"
    - "on network coding for sum networks"
    - "two way networks when adaptation is useless"

Connected through hyperedge HE-4 (induced by source paper "feedback capacity of stationary gaussian channels further examined"):
  with members:
    - "on the feedback capacity of power constrained gaussian noise channels with memory"
    - "feedback capacity of stationary gaussian channels"

Connected through hyperedge HE-5 (induced by source paper "communicating over filter and forward relay networks with channel output feedback"):
  with members:
    - "capacity of all nine models of channel output feedback for the two user interference channel"
    - "a joint time invariant filtering approach to the linear gaussian relay problem"
    - "the approximate capacity of the gaussian n relay diamond network"
    - "concatenated coding for the awgn channel with noisy feedback"
    - "feedback capacity of stationary gaussian channels"

Connected through hyperedge HE-6 (induced by source paper "capacity of binary state symmetric channel with and without feedback and transmission cost"):
  with members:
    - "information nonanticipative rate distortion function and its applications"
    - "applications of information nonanticipative rate distortion function"
    - "capacity of a post channel with and without feedback"
    - "nonanticipative rate distortion function for general source channel matching"
    - "directed information on abstract spaces properties and extremum problems"
    - "capacity and coding for the ising channel with feedback"
    - "on directed information and gambling"

Connected through hyperedge HE-7 (induced by source paper "capacity achieving feedback schemes for gaussian finite state markov channels with channel state information"):
  with members:
    - "convergence of fundamental limitations in feedback communication estimation and feedback control over gaussian channels"
    - "on the capacity of time varying channels with periodic feedback"
    - "the capacity of channels with feedback"
    - "coding for the feedback gel fand pinsker channel and the feedforward wyner ziv source"

Connected through hyperedge HE-8 (induced by source paper "perfect output feedback in the two user decentralized interference channel"):
  with members:
    - "noisy channel output feedback capacity of the linear deterministic interference channel"
    - "quality of service provisioning in decentralized networks a satisfaction equilibrium approach"
    - "wireless network information flow a deterministic approach"
    - "on the nash equilibria in decentralized parallel interference channels"
    - "feedback capacity of the gaussian interference channel to within 2 bits"
    - "shannon meets nash on the interference channel"
    - "on the capacity of the discrete memoryless broadcast channel with feedback"
    - "a new outer bound and the noisy interference sum rate capacity for gaussian interference channels"

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)