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: "coded power control performance analysis"
  Abstract: In this paper, we introduce the general concept of coded power control (CPC) in a particular setting of the interference channel. Roughly, the idea of CPC consists in embedding information (about a random state) into the transmit power levels themselves: in this new framework, provided the power levels of a given transmitter can be observed (through a noisy channels) by other transmitters, a sequence of power levels of the former can therefore be used to coordinate the latter. To assess the limiting performance of CPC (and therefore the potential performance brought by this new approach), we derive, as a first step towards many extensions of the present work, a general result which not only concerns power control (PC) but also any scenario involving two decision-makers (DMs) which communicate through their actions and have the following information and decision structures. We assume that the DMs want to maximize the average of an arbitrarily chosen instantaneous payoff function which depends on the DMs' actions and the state realization. DM 1 is assumed to know non-causally the state (e.g., the channel state) which affects the common payoff while the other, say DM 2, has only a strictly causal knowledge of it. DM 1 can only use its own actions (e.g., power levels) to inform DM 2 about its best action in terms of payoff. Importantly, DM 2 can only monitor the actions of DM 1 imperfectly and DM 1 does not observe DM 2. The latter assumption leads us to exploiting Shannon-theoretic tools in order to generalize an existing theorem which provides the information constraint under which the payoff is maximized. The derived result is then exploited to fully characterize the performance of good CPC policies for a given instance of the interference channel.

Connected through hyperedge HE-1 (induced by source paper "distributed power control with partial channel state information performance characterization and design"):
  with members:
    - "interference coordination via power domain channel estimation"
    - "ultra dense small cell networks turning density into energy efficiency"
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"
    - "coordination in distributed networks via coded actions with application to power control"
    - "robust precoding for network mimo with hierarchical csit"
    - "a game theoretic analysis for energy efficient heterogeneous networks"
    - "mac with action dependent state information at one encoder"
    - "framework for link level energy efficiency optimization with informed transmitter"

Connected through hyperedge HE-2 (induced by source paper "interference coordination via power domain channel estimation"):
  with members:
    - "ultra dense small cell networks turning density into energy efficiency"
    - "coordination in distributed networks via coded actions with application to power control"
    - "resource allocation for energy efficient 3 way relay channels"
    - "compressive rate estimation with applications to device to device communications"
    - "energy aware competitive power allocation for heterogeneous networks under qos constraints"
    - "crawford sobel meet lloyd max on the grid"
    - "power optimization in random wireless networks"
    - "distributed learning policies for power allocation in multiple access channels"

Connected through hyperedge HE-3 (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"
    - "empirical coordination for joint source channel coding"
    - "polar coding for the broadcast channel with confidential messages a random binning analogy"
    - "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-4 (induced by source paper "coordination in distributed networks via coded actions with application to power control"):
  with members:
    - "a cross layer approach for distributed energy efficient power control in interference networks"
    - "strong coordination over a line network"
    - "empirical coordination in a triangular multiterminal network"
    - "distributed channel synthesis"
    - "strong coordination with polar codes"
    - "coordination via a relay"
    - "action dependent strictly causal state communication"
    - "distributed learning policies for power allocation in multiple access channels"

Connected through hyperedge HE-5 (induced by source paper "empirical coordination with two sided state information and correlated source and state"):
  with members:
    - "coordination in distributed networks via coded actions with application to power control"
    - "empirical coordination with channel feedback and strictly causal or causal encoding"
    - "joint empirical coordination of source and channel"
    - "correlation between channel state and information source with empirical coordination constraint"
    - "causal state communication"
    - "strong coordination with polar codes"
    - "hybrid codes needed for coordination over the point to point channel"
    - "coordination using implicit communication"

Connected through hyperedge HE-6 (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"
    - "empirical coordination for joint source channel coding"
    - "joint empirical coordination of source and channel"
    - "causal state communication"
    - "coordination using implicit communication"

Connected through hyperedge HE-7 (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"
    - "strong coordination over multi hop line networks"
    - "zero error coordination"
    - "empirical coordination with two sided state information and correlated source and state"

Connected through hyperedge HE-8 (induced by source paper "empirical coordination for joint source channel coding"):
  with members:
    - "coordination in distributed networks via coded actions with application to power control"
    - "secure cascade channel synthesis"
    - "strong coordination over multi hop line networks"
    - "zero error coordination"
    - "coordination in state dependent distributed networks the two agent case"
    - "correlation between channel state and information source with empirical coordination constraint"
    - "strong coordination over a line network"
    - "empirical processes typical sequences and coordinated actions in standard borel spaces"

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)