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: "sending a bivariate gaussian source over a gaussian mac with feedback"
  Abstract: We study the power-versus-distortion trade-off for the transmission of a memoryless bivariate Gaussian source over a two-to-one Gaussian multiple-access channel with perfect causal feedback. In this problem, each of two separate transmitters observes a different component of a memoryless bivariate Gaussian source as well as the feedback from the channel output of the previous time-instants. Based on the observed source sequence and the feedback, each transmitter then describes its source component to the common receiver via an average-power constrained Gaussian multiple-access channel. From the resulting channel output, the receiver wishes to reconstruct both source components with the least possible expected squared-error distortion. We study the set of distortion pairs that can be achieved by the receiver on the two source components. #R##N#We present sufficient conditions and necessary conditions for the achievability of a distortion pair. These conditions are expressed in terms of the source correlation and of the signal-to-noise ratio (SNR) of the channel. In several cases the necessary conditions and sufficient conditions coincide. This allows us to show that if the channel SNR is below a certain threshold, then an uncoded transmission scheme that ignores the feedback is optimal. Thus, below this SNR-threshold feedback is useless. We also derive the precise high-SNR asymptotics of optimal schemes.

Connected through hyperedge HE-1 (induced by source paper "on the energy distortion tradeoff of gaussian broadcast channels with feedback"):
  with members:
    - "on the ozarow leung scheme for the gaussian broadcast channel with feedback"
    - "interactive schemes for the awgn channel with noisy feedback"
    - "insufficiency of linear feedback schemes in gaussian broadcast channels with common message"
    - "mimo mac bc duality with linear feedback coding schemes"
    - "source channel coding theorems for the multiple access relay channel"
    - "lqg control approach to gaussian broadcast channels with feedback"
    - "optimality and approximate optimality of source channel separation in networks"
    - "variations on a theme by schalkwijk and kailath"

Connected through hyperedge HE-2 (induced by source paper "distributed sensing and transmission of sporadic random samples over a multiple access channel"):
  with members:
    - "five disruptive technology directions for 5g"
    - "distortion bounds and two way protocols for one shot transmission of correlated random variables"
    - "robust analog function computation via wireless multiple access channels"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "sending a bivariate gaussian source over a gaussian mac with unidirectional conferencing encoders"):
  with members:
    - "the gaussian mac with conferencing encoders"
    - "rate region of the quadratic gaussian two encoder source coding problem"

Connected through hyperedge HE-4 (induced by source paper "hybrid coding an interface for joint source channel coding and network communication"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "on the separation of lossy source network coding and channel coding in wireline networks"
    - "communication to within a fidelity criterion over unknown networks by reduction to reliable communication problems over unknown networks"
    - "optimality and approximate optimality of source channel separation in networks"
    - "the achievable distortion region of bivariate gaussian source on gaussian broadcast channel"
    - "correlated sources over broadcast channels"
    - "wyner ziv coding over broadcast channels digital schemes"
    - "outer bounds on the admissible source region for broadcast channels with dependent sources"

Connected through hyperedge HE-5 (induced by source paper "distortion bounds and two way protocols for one shot transmission of correlated random variables"):
  with members:
    - "sending a bi variate gaussian source over a gaussian mac"
    - "sending a bi variate gaussian source over a gaussian mac"

Connected through hyperedge HE-6 (induced by source paper "comparison of spearman s rho and kendall s tau in normal and contaminated normal models"):
  with members:
    - "broadcasting correlated gaussians"

Connected through hyperedge HE-7 (induced by source paper "distributed joint source channel coding on a multiple access channel with side information"):
  with members:
    - "lattice quantization with side information codes asymptotics and applications in sensor networks"
    - "sending a bi variate gaussian source over a gaussian mac"
    - "sending a bi variate gaussian source over a gaussian mac"
    - "coding for the feedback gel fand pinsker channel and the feedforward wyner ziv source"
    - "network information flow with correlated sources"
    - "rate region of the quadratic gaussian two encoder source coding problem"

Connected through hyperedge HE-8 (induced by source paper "distributed joint source channel coding on a multiple access channel with side information"):
  with members:
    - "lattice quantization with side information codes asymptotics and applications in sensor networks"
    - "sending a bi variate gaussian source over a gaussian mac"
    - "sending a bi variate gaussian source over a gaussian mac"

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)