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 "poisson group testing a probabilistic model for boolean compressed sensing"):
  Abstract: We introduce a novel probabilistic group testing framework, termed Poisson group testing, in which the number of defectives follows a right-truncated Poisson distribution. The Poisson model has a number of new applications, including dynamic testing with diminishing relative rates of defectives. We consider both nonadaptive and semi-adaptive identification methods. For nonadaptive methods, we derive a lower bound on the number of tests required to identify the defectives with a probability of error that asymptotically converges to zero; in addition, we propose test matrix constructions for which the number of tests closely matches the lower bound. For semi-adaptive methods, we describe a lower bound on the expected number of tests required to identify the defectives with zero error probability. In addition, we propose a stage-wise reconstruction algorithm for which the expected number of tests is only a constant factor away from the lower bound. The methods rely only on an estimate of the average number of defectives, rather than on the individual probabilities of subjects being defective.

Includes members:
  Member "limits on support recovery with probabilistic models an information theoretic framework":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "group testing an information theory perspective")
  Member "group testing with prior statistics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "group testing an information theory perspective")
  Member "lectures on designing screening experiments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal multistage group testing algorithm for 3 defectives")
  Member "the capacity of non identical adaptive group testing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rates of adaptive group testing in the linear regime")
  Member "group testing algorithms bounds and simulations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "serial quantization for sparse time sequences")
  Member "semiquantitative group testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "group testing with runlength constraints for topological molecular storage")
  Member "non adaptive group testing explicit bounds and novel algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "serial quantization for sparse time sequences")
  Member "boolean compressed sensing and noisy group testing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "serial quantization for sparse time sequences")

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)