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 "searching for anomalies over composite hypotheses"):
  Abstract: The problem of detecting anomalies in multiple processes is considered. We consider a composite hypothesis case, in which the measurements drawn when observing a process follow a common distribution with an unknown parameter (vector), whose value lies in normal or abnormal parameter spaces, depending on its state. The objective is a sequential search strategy that minimizes the expected detection time subject to an error probability constraint. We develop a deterministic search algorithm with the following desired properties. First, when no additional side information on the process states is known, the proposed algorithm is asymptotically optimal in terms of minimizing the detection delay as the error probability approaches zero. Second, when the parameter value under the null hypothesis is known and equal for all normal processes, the proposed algorithm is asymptotically optimal as well, with better detection time determined by the true null state. Third, when the parameter value under the null hypothesis is unknown, but is known to be equal for all normal processes, the proposed algorithm is consistent in terms of achieving error probability that decays to zero with the detection delay. Finally, an explicit upper bound on the error probability under the proposed algorithm is established for the finite sample regime. Extensive experiments on synthetic dataset and DARPA intrusion detection dataset are conducted, demonstrating strong performance of the proposed algorithm over existing methods.

Includes members:
  Member "active anomaly detection in heterogeneous processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep actor critic reinforcement learning for anomaly detection")
  Member "learning to detect an oddball target":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to detect an odd markov arm")
  Member "optimal index policies for anomaly localization in resource constrained cyber systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "utility maximizing sequential sensing over a finite horizon")
  Member "asymptotically optimal anomaly detection via sequential testing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spectrum and energy efficient multiple access for detection in wireless sensor 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 "anomaly detection in hierarchical data streams under unknown models")
  Member "controlled sensing for multihypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "active hypothesis testing beyond chernoff stein")
  Member "sequential testing for sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "identifying outliers in large matrices via randomized adaptive compressive sampling")

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.IT(Information Theory)