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: "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.

Connected through hyperedge HE-1 (induced by source paper "probabilistic group testing under sum observations a parallelizable 2 approximation for entropy loss"):
  with members:
    - "group testing with prior statistics"
    - "info greedy sequential adaptive compressed sensing"
    - "active testing for face detection and localization"
    - "improved combinatorial group testing algorithms for real world problem sizes"
    - "an informational approach to the global optimization of expensive to evaluate functions"

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)