human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "utility maximizing sequential sensing over a finite horizon"):
  Abstract: We consider the problem of optimally utilizing   $N$    resources, each in an unknown binary state. The state of each resource can be inferred from state-dependent noisy measurements. Depending on its state, utilizing a resource results in either a reward or a penalty per unit time. The objective is a sequential strategy governing the decision of sensing and exploitation at each time to maximize the expected utility (i.e., total reward minus total penalty and sensing cost) over a finite horizon   $L$  . We formulate the problem as a partially observable Markov decision process and show that the optimal strategy is based on two time-varying thresholds for each resource and an optimal selection rule to sense a particular resource. Since a full characterization of the optimal strategy is generally intractable, we develop a low-complexity policy that is shown by simulations to offer a near optimal performance. This problem finds applications in opportunistic spectrum access, marketing strategies, and other sequential resource allocation problems.

Includes members:
  Member "reliability of sequential hypothesis testing can be achieved by an almost fixed length test":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "second order asymptotics of sequential hypothesis testing")
  Member "opportunistic advertisement scheduling in live social media a multiple stopping time pomdp approach":
    also connected through other hyperedges:
      (none)
  Member "optimal index policies for anomaly localization in resource constrained cyber systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "searching for anomalies over composite hypotheses")
  Member "asymptotically optimal anomaly detection via sequential testing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "searching for anomalies over composite hypotheses")
  Member "censored truncated sequential spectrum sensing for cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "random interruptions in cooperation for spectrum sensing in cognitive radio networks")
  Member "active sequential hypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "decentralized sequential active hypothesis testing and the mac feedback capacity")
  Member "sequentiality and adaptivity gains in active hypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "searching for anomalies over composite hypotheses")
  Member "controlled sensing for multihypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "searching for anomalies over composite hypotheses")

Question: Please tell me which class the source paper 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.SY(Systems and Control)