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: "optimal index policies for anomaly localization in resource constrained cyber systems"
  Abstract: Abstract : The problem of anomaly localization in a resource-constrained cyber system is considered. Each anomalous component of the system incurs a cost per unit time until its anomaly is identified and fixed. Different anomalous components may incur different costs depending on their criticality to the system. Due to resource constraints, only one component can be probed at each given time. The observations from a probed component are realizations drawn from two different distributions depending on whether the component is normal or anomalous. The objective is a probing strategy that minimizes the total expected cost, incurred by all the components during the detection process, under reliability constraints. We consider both independent and exclusive models. In the former, each component can be abnormal with a certain probability independent of other components. In the latter, one and only one component is abnormal. We develop optimal index policies under both models. The proposed index policies apply to a more general case where a subset (more than one) of the components can be probed simultaneously. The problem under study also finds applications in spectrum scanning in cognitive radio networks and event detection in sensor networks.

Connected through hyperedge HE-1 (induced by source paper "searching for anomalies over composite hypotheses"):
  with members:
    - "active anomaly detection in heterogeneous processes"
    - "learning to detect an oddball target"
    - "asymptotically optimal anomaly detection via sequential testing"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "sequential testing for sparse recovery"

Connected through hyperedge HE-2 (induced by source paper "utility maximizing sequential sensing over a finite horizon"):
  with members:
    - "reliability of sequential hypothesis testing can be achieved by an almost fixed length test"
    - "opportunistic advertisement scheduling in live social media a multiple stopping time pomdp approach"
    - "asymptotically optimal anomaly detection via sequential testing"
    - "censored truncated sequential spectrum sensing for cognitive radio networks"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"

Connected through hyperedge HE-3 (induced by source paper "active anomaly detection in heterogeneous processes"):
  with members:
    - "utility maximizing sequential sensing over a finite horizon"
    - "learning to detect an oddball target"
    - "asymptotically optimal anomaly detection via sequential testing"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"

Connected through hyperedge HE-4 (induced by source paper "asymptotically optimal anomaly detection via sequential testing"):
  with members:
    - "active hypothesis testing for quickest anomaly detection"
    - "universal outlier hypothesis testing"
    - "quickest search over multiple sequences with mixed observations"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "sequential testing for sparse recovery"

Connected through hyperedge HE-5 (induced by source paper "active hypothesis testing for quickest anomaly detection"):
  with members:
    - "universal outlier hypothesis testing"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "quickest change detection of a markov process across a sensor array"
    - "one shot schemes for decentralized quickest change detection"

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)