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: "non bayesian quickest change detection with stochastic sample right constraints"
  Abstract: In this paper, we study the design and analysis of optimal detection scheme for sensors that are deployed to monitor the change in the environment and are powered by energy harvested from the environment. In this type of applications, detection delay is of paramount importance. We model this problem as quickest change detection problem with stochastic energy constraints. In particular, a wireless sensor powered by renewable energy takes observations from a random sequence, whose distribution will change at an unknown time. Such a change implies events of interest. The energy in the sensor is consumed by taking observations and is replenished randomly. The sensor cannot take observations if there is no energy left in the battery. Our goal is to design optimal power allocation and detection schemes to minimize the worst case detection delay, which is the difference between the time when the change occurs and the time when an alarm is raised. Two types of average run length (ARL) constraints, namely an algorithm level ARL constraint and a system level ARL constraint, are considered. We propose a low complexity scheme in which the energy allocation rule is to spend energy to take observations as long as the battery is not empty and the detection scheme is the Cumulative Sum test. We show that this scheme is optimal for the formulation with the algorithm level ARL constraint and is asymptotically optimal for the formulations with the system level ARL constraint.

Connected through hyperedge HE-1 (induced by source paper "age of information minimization for an energy harvesting source with updating erasures with and without feedback"):
  with members:
    - "performance characterization using aoi in a single loop networked control system"
    - "the age of information in networks moments distributions and sampling"
    - "finite time distributed state estimation over time varying graphs exploiting the age of information"
    - "optimal status updating for an energy harvesting sensor with a noisy channel"
    - "coded status updates in an energy harvesting erasure channel"
    - "age minimal online policies for energy harvesting sensors with random battery recharges"
    - "sending information through status updates"
    - "stochastic control with stale information part i fully observable systems"

Connected through hyperedge HE-2 (induced by source paper "optimal status update for age of information minimization with an energy harvesting source"):
  with members:
    - "timeliness in lossless block coding"
    - "scheduling status updates to minimize age of information with an energy harvesting sensor"
    - "timely updates over an erasure channel"
    - "status updates through m g 1 1 queues with harq"
    - "age optimal information updates in multihop networks"
    - "age and value of information non linear age case"
    - "age minimal transmission in energy harvesting two hop networks"
    - "remote estimation of the wiener process over a channel with random delay"

Connected through hyperedge HE-3 (induced by source paper "quickest change detection in adaptive censoring sensor networks"):
  with members:
    - "quickest change detection with a censoring sensor in the minimax setting"
    - "bayesian quickest change point detection with sampling right constraints"
    - "decentralized sequential change detection using physical layer fusion"

Connected through hyperedge HE-4 (induced by source paper "quickest change detection with a censoring sensor in the minimax setting"):
  with members:
    - "bayesian quickest change point detection with sampling right constraints"

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)