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: "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.

Connected through hyperedge HE-1 (induced by source paper "quantitative group testing in the sublinear regime"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "information theoretic and algorithmic thresholds for group testing"
    - "information theoretic thresholds from the cavity method"
    - "the capacity of bernoulli nonadaptive group testing"
    - "decoding from pooled data phase transitions of message passing"
    - "on the optimality of some group testing algorithms"
    - "improved group testing rates with constant column weight designs"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"

Connected through hyperedge HE-2 (induced by source paper "non adaptive quantitative group testing using irregular sparse graph codes"):
  with members:
    - "optimal multiclass overfitting by sequence reconstruction from hamming queries"
    - "quantitative group testing in the sublinear regime"
    - "a simple and efficient strategy for the coin weighing problem with a spring scale"
    - "decoding from pooled data phase transitions of message passing"
    - "nonadaptive group testing with random set of defectives"
    - "optimal nested test plan for combinatorial quantitative group testing"

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)