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 over multi hop line networks"
  Abstract: We analyze the problem of strong coordination over a multi-hop line network where the node initiating the coordination is a terminal network node. We provide a characterization of the capacity region when the initiating node possesses unlimited local randomness and intermediate nodes operate under a functional regime. In this regime, next-hop messages are created only using common randomness and previous-hop incoming messages, i.e., local randomness at intermediate nodes is only used for generating actions.

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"
    - "coordination using individually shared randomness"
    - "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"

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"
    - "coordination using individually shared randomness"
    - "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 a line network"
    - "common information and secret key capacity"

Connected through hyperedge HE-3 (induced by source paper "joint coordination channel coding for strong coordination over noisy channels based on polar codes"):
  with members:
    - "strong coordination of signals and actions over noisy channels"
    - "strong coordination over noisy channels is separation sufficient"
    - "empirical and strong coordination via soft covering with polar codes"
    - "polar coding for empirical coordination of signals and actions over noisy channels"
    - "polar codes for broadcast channels"
    - "asymmetric error correction and flash memory rewriting using polar codes"
    - "polar coding for the broadcast channel with confidential messages a random binning analogy"
    - "strong coordination over a line network"

Connected through hyperedge HE-4 (induced by source paper "strong coordination over noisy channels is separation sufficient"):
  with members:
    - "when is it possible to simulate a dmc channel from another"
    - "strong coordination over a line network"
    - "empirical coordination in a triangular multiterminal network"
    - "distributed channel synthesis"
    - "channel simulation via interactive communications"
    - "coordination via a relay"
    - "hybrid codes needed for coordination over the point to point channel"
    - "generating dependent random variables over networks"

Connected through hyperedge HE-5 (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"
    - "empirical coordination for joint source channel coding"
    - "joint empirical coordination of source and channel"

Connected through hyperedge HE-6 (induced by source paper "lossy compression with near uniform encoder outputs"):
  with members:
    - "the likelihood encoder for source coding"
    - "second order asymptotics in fixed length source coding and intrinsic randomness"

Connected through hyperedge HE-7 (induced by source paper "secure cascade channel synthesis"):
  with members:
    - "secrecy in cascade networks"
    - "the likelihood encoder for lossy source compression"
    - "strong coordination over a line network"
    - "distributed channel synthesis"
    - "rate distortion theory for secrecy systems"
    - "achievability proof via output statistics of random binning"
    - "secure channel simulation"
    - "channel simulation via interactive communications"

Connected through hyperedge HE-8 (induced by source paper "joint empirical coordination of source and channel"):
  with members:
    - "strong coordination of signals and actions over noisy channels"
    - "information design for strategic coordination of autonomous devices with non aligned utilities"
    - "polar coding for empirical coordination of signals and actions over noisy channels"
    - "coordination in distributed networks via coded actions with application to power control"
    - "secure cascade channel synthesis"
    - "zero error coordination"
    - "empirical coordination with two sided state information and correlated source and state"
    - "empirical coordination with channel feedback and strictly causal or causal encoding"

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)