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 "secure adaptive group testing"):
  Abstract: Group Testing (GT) addresses the problem of identifying a small subset of defective items from a large population, by grouping items into as few test pools as possible. In Adaptive GT (AGT), outcomes of previous tests can influence the makeup of future tests. This scenario has been studied from an information theoretic point of view. Aldridge $2012$ showed that in the regime of a few defectives, adaptivity does not help much, as the number of tests required for identification of the set of defectives is essentially the same as for non-adaptive GT. #R##N#Secure GT considers a scenario where there is an eavesdropper who may observe a fraction $\delta$ of the outcomes, and should not be able to infer the status of the items. In the non-adaptive scenario, the number of tests required is $1/(1-\delta)$ times the number of tests without the secrecy constraint. #R##N#In this paper, we consider Secure Adaptive GT. Specifically, when an adaptive algorithm has access to a private feedback link of rate $R_f$, we prove that the number of tests required for both correct reconstruction at the legitimate user, with high probability, and negligible mutual information at the eavesdropper is $1/min\{1,1-\delta+R_f\}$ times the number of tests required with no secrecy constraint. Thus, unlike non-secure GT, where an adaptive algorithm has only a mild impact, under a security constraint it can significantly boost performance. A key insight is that not only the adaptive link should disregard test results and send keys, these keys should be enhanced through a "secret sharing" scheme before usage.

Includes members:
  Member "data aggregation over multiple access wireless sensors network":
    also connected through other hyperedges:
      (none)
  Member "secure group testing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "data aggregation over multiple access wireless sensors network")
  Member "information theoretic bounds for adaptive sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure group testing")
  Member "the capacity of non identical adaptive group testing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rates of adaptive group testing in the linear regime")
  Member "near optimal adaptive compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "wiretap channel with causal state information and secure rate limited feedback":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "feedback coding schemes for the broadcast channel with mutual secrecy requirement")
  Member "the capacity of adaptive group testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the all or nothing behavior of bernoulli group testing")
  Member "adaptive group testing as channel coding with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure group testing")

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)