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 "quantitative group testing for heavy hitter detection"):
  Abstract: We consider the quantitative group testing problem where the objective is to identify defective items in a given population based on results of tests performed on subsets of the population. Under the quantitative group testing model, the result of each test reveals the number of defective items in the tested group. The minimum number of tests achievable by nested test plans was established by Aigner and Schughart in 1985 within a minimax framework. The optimal nested test plan offering this performance, however, was not obtained. In this work, we establish the optimal nested test plan in closed form. This optimal nested test plan is also asymptotically (as the population size grows to infinity) optimal among all test plans. We then focus on the application of heavy hitter detection problem for traffic monitoring and anomaly detection in the Internet and other communication networks. For such applications, it is often the case that a few abnormal traffic flows with exceptionally high volume (referred to as heavy hitters) make up most of the traffic seen by the entire network. Since the volume of heavy hitters is much higher than that of normal flows, the number of heavy hitters in a group of flows can be accurately estimated from the aggregated traffic load. Other potential applications include detecting idle channels in the radio spectrum in the high SNR regime.

Includes members:
  Member "probabilistic group testing under sum observations a parallelizable 2 approximation for entropy loss":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed probabilistic bisection search using social learning")
  Member "group testing algorithms bounds and simulations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "serial quantization for sparse time sequences")
  Member "semiquantitative group testing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "group testing with runlength constraints for topological molecular storage")
  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 "boolean compressed sensing and noisy group testing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "serial quantization for sparse time sequences")
  Member "group testing with probabilistic tests theory design and application":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the all or nothing behavior of bernoulli group testing")
  Member "graph constrained group testing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a framework for generalized group testing with inhibitors and its potential application in neuroscience")
  Member "derandomization and group testing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "group testing an information theory perspective")

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)