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 "sparse graph codes for non adaptive quantitative group testing"):
  Abstract: This paper considers the problem of quantitative group testing with a non-adaptive testing strategy. In a quantitative group testing scheme, a set of tests are designed to identify defective items among a large population of items, where the outcome of a test shows the number of defective items in the tested group. There are two models for the defective items: (i)$~$deterministic, and (ii) randomized. In the deterministic model, the exact number of the defective items, $K$, is known, whereas in the randomized model each item is defective with probability $\frac{K}{N}$, independent of the other items, where $N$ is the total number of items. In this work, we propose a non-adaptive quantitative group testing algorithm using sparse graph codes over bi-regular bipartite graphs with left-degree $\ell$ and right degree $r$ and binary $t$-error-correcting BCH codes. We show that for both the deterministic and randomized models, our algorithm requires at most ${m=c(t)K(t\log(\frac{\ell N}{c(t)K}+1)+1)+1}$ tests to recover all the defective items with probability approaching one (as $N$ and $K$ grow unbounded), where $c(t)$ is a constant that depends only on $t$. The results of our theoretical analysis reveal that using a $t$-error-correcting binary BCH code for $t\in \{1,2,3\}$, when compared to $t\geq 4$, leads to a fewer number of tests. Simulation results show that the proposed strategy significantly reduces the required number of tests for identifying all the defective items with probability approaching one compared to a recently proposed scheme.

Includes members:
  Member "a simple and efficient strategy for the coin weighing problem with a spring scale":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non adaptive quantitative group testing using irregular sparse graph codes")
  Member "a fast noniterative algorithm for compressive sensing using binary measurement matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressed csi feedback with learned measurement matrix for mmwave massive mimo")
  Member "group testing using left and right regular sparse graph codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sublinear time non adaptive group testing with o k log n tests via bit mixing coding")
  Member "saffron a fast efficient and robust framework for group testing based on sparse graph codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the all or nothing behavior of bernoulli group testing")
  Member "sub linear time support recovery for compressed sensing using sparse graph codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning mixtures of sparse linear regressions using sparse graph codes")
  Member "optimal nested test plan for combinatorial quantitative group testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "group testing an information theory perspective")
  Member "compressed sensing with sparse binary matrices instance optimal error guarantees in near optimal time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "derandomized compressed sensing with nonuniform guarantees for 120001 1 recovery")

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)