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: "empirical coordination for joint source channel coding"
  Abstract: We investigate the problem of simultaneous transmission and coordination for a point-to-point communication model. The encoder and decoder choose their symbols/actions (i.e. channel input and decoder output) in order to transmit information and to coordinate themselves. This paradigm has promising repercussions for future decentralized networks. Exploiting the coordination capabilities, devices can implement decentralized policies that are coordinated. Exploiting transmission capabilities, devices choose their actions (power control, the channel allocation) in order to encode additional embedded information (channel state information, future allocations). The feasible trade-offs between transmissions and coordinations are characterized using joint probability distributions over the source and channel symbols. Encoder and decoder implement a coding scheme such that the empirical frequency of symbols are close to target joint probability distribution. We characterize the set of achievable probability distribution for a point-to-point source-channel model with strictly-causal and causal decoding. Compared to classical coding results, the coordination requirement induces a stronger information constraint. We determine optimal trade-offs between transmission and coordination by maximizing a utility function over the set of achievable probability distribution.

Connected through hyperedge HE-1 (induced by source paper "empirical and strong coordination via soft covering with polar codes"):
  with members:
    - "secure multiplex coding with dependent and non uniform multiple messages"
    - "f divergence inequalities"
    - "covert communication over noisy channels a resolvability perspective"
    - "coordination in distributed networks via coded actions with application to power control"
    - "bounds among f divergences"
    - "semantic security capacity for wiretap channels of type ii"
    - "strong coordination over multi hop line networks"
    - "joint empirical coordination of source and channel"

Connected through hyperedge HE-2 (induced by source paper "polar coding for empirical coordination of signals and actions over noisy channels"):
  with members:
    - "coordination in distributed networks via coded actions with application to power control"
    - "empirical coordination with two sided state information and correlated source and state"
    - "polar coding for the broadcast channel with confidential messages a random binning analogy"
    - "coded power control performance analysis"
    - "correlation between channel state and information source with empirical coordination constraint"
    - "polar coding for secret key generation"
    - "universal polar codes"
    - "strong coordination with polar codes"

Connected through hyperedge HE-3 (induced by source paper "coordination in state dependent distributed networks the two agent case"):
  with members:
    - "coordination in distributed networks via coded actions with application to power control"
    - "joint empirical coordination of source and channel"
    - "coded power control performance analysis"
    - "causal state communication"
    - "coordination using implicit communication"

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)