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: "coordination using individually shared randomness"
  Abstract: Two processors output correlated sequences using the help of a coordinator with whom they individually share independent randomness. For the case of unlimited shared randomness, we characterize the rate of communication required from the coordinator to the processors over a broadcast link. We also give an achievable trade-off between the communication and shared randomness rates.

Connected through hyperedge HE-1 (induced by source paper "optimal communication rates and combinatorial properties for distributed simulation"):
  with members:
    - "domain compression and its application to randomness optimal distributed goodness of fit"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "distributed simulation and distributed inference"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "resource efficient common randomness and secret key schemes"
    - "on the public communication needed to achieve sk capacity in the multiterminal source model"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "strong coordination over multi hop line networks"

Connected through hyperedge HE-2 (induced by source paper "optimal communication rates for zero error distributed simulation under blackboard communication protocols"):
  with members:
    - "domain compression and its application to randomness optimal distributed goodness of fit"
    - "distributed simulation and distributed inference"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "on the public communication needed to achieve sk capacity in the multiterminal source model"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "strong coordination over multi hop line networks"
    - "strong coordination over a line network"
    - "common information and secret key capacity"

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)