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: "matched multiuser gaussian source channel communications via uncoded schemes"
  Abstract: We investigate whether uncoded schemes are optimal for Gaussian sources on multiuser Gaussian channels. Particularly, we consider two problems: the first is to send correlated Gaussian sources on a Gaussian broadcast channel where each receiver is interested in reconstructing only one source component (or one specific linear function of the sources) under the mean squared error distortion measure; the second is to send correlated Gaussian sources on a Gaussian multiple-access channel, where each transmitter observes a noisy combination of the source, and the receiver wishes to reconstruct the individual source components (or individual linear functions) under the mean squared error distortion measure. It is shown that when the channel parameters match certain general conditions, the induced distortion tuples are on the boundary of the achievable distortion region, and thus optimal. Instead of following the conventional approach of attempting to characterize the achievable distortion region, we ask the question whether and how a match can be effectively determined. This decision problem formulation helps to circumvent the difficult optimization problem often embedded in region characterization problems, and it also leads us to focus on the critical conditions in the outer bounds that make the inequalities become equalities, which effectively decouple the overall problem into several simpler sub-problems. Optimality results previously unknown in the literature are obtained using this novel approach. As a byproduct of the investigation, novel outer bounds are derived for these two problems.

Connected through hyperedge HE-1 (induced by source paper "optimal communication strategies in networked cyber physical systems with adversarial elements"):
  with members:
    - "information theoretic approach to strategic communication as a hierarchical game"
    - "power distortion metrics for path planning over gaussian sensor networks"
    - "optimal power allocation for parameter tracking in a distributed amplify and forward sensor network"
    - "gaussian sensor networks with adversarial nodes"
    - "on scaling laws of diversity schemes in decentralized estimation"
    - "distributed source coding in the presence of byzantine sensors"

Connected through hyperedge HE-2 (induced by source paper "communicating correlated sources over mac and interference channels i separation based schemes"):
  with members:
    - "communicating correlated sources over an interference channel"
    - "broadcasting correlated vector gaussians"
    - "on joint source channel coding for correlated sources over multiple access relay channels"
    - "an achievable rate distortion region for the multiple descriptions problem"
    - "a new data processing inequality and its applications in distributed source and channel coding"
    - "optimality and approximate optimality of source channel separation in networks"
    - "interference channels with arbitrarily correlated sources"
    - "distributed joint source channel coding on a multiple access channel with side information"

Connected through hyperedge HE-3 (induced by source paper "on transmitting correlated sources over a mac"):
  with members:
    - "on joint source channel coding for correlated sources over multiple access relay channels"
    - "interference channels with arbitrarily correlated sources"

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)