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: "strong coordination of signals and actions over noisy channels with two sided state information"
  Abstract: We consider a network of two nodes separated by a noisy channel with two-sided state information, in which the input and output signals have to be coordinated with the source and its reconstruction. In the case of non-causal encoding and decoding, we propose a joint source-channel coding scheme and develop inner and outer bounds for the strong coordination region. While the inner and outer bounds do not match in general, we provide a complete characterization of the strong coordination region in three particular cases: i) when the channel is perfect; ii) when the decoder is lossless; and iii) when the random variables of the channel are independent from the random variables of the source. Through the study of these special cases, we prove that the separation principle does not hold for joint source-channel strong coordination. Finally, in the absence of state information, we show that polar codes achieve the best known inner bound for the strong coordination region, which therefore offers a constructive alternative to random binning and coding proofs.

Connected through hyperedge HE-1 (induced by source paper "strategic communication with side information at the decoder"):
  with members:
    - "persuasion with limited communication capacity"
    - "information design for strategic coordination of autonomous devices with non aligned utilities"
    - "information theoretic approach to strategic communication as a hierarchical game"
    - "on the role of side information in strategic communication"
    - "persuasion with limited communication"
    - "algorithmic bayesian persuasion"
    - "empirical coordination with two sided state information and correlated source and state"
    - "the henchman problem measuring secrecy by the minimum distortion in a list"

Connected through hyperedge HE-2 (induced by source paper "fixed length strong coordination"):
  with members:
    - "strong coordination of signals and actions over noisy channels"
    - "non asymptotic output statistics of random binning and its applications"
    - "achievability proof via output statistics of random binning"
    - "fixed length lossy compression in the finite blocklength regime"

Connected through hyperedge HE-3 (induced by source paper "strong coordination over noisy channels with strictly causal encoding"):
  with members:
    - "polar codes for empirical coordination over noisy channels with strictly causal encoding"
    - "strong coordination of signals and actions over noisy channels"
    - "polar coding for the broadcast channel with confidential messages a random binning analogy"
    - "polar coding for secret key generation"
    - "simulation of a channel with another channel"
    - "distributed channel synthesis"
    - "achievability proof via output statistics of random binning"
    - "hybrid codes needed for coordination over the point to point channel"

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)