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: "group testing using left and right regular sparse graph codes"
  Abstract: We consider the problem of non-adaptive group testing of $N$ items out of which $K$ or less items are known to be defective. We propose a testing scheme based on left-and-right-regular sparse-graph codes and a simple iterative decoder. We show that for any arbitrarily small $\epsilon>0$ our scheme requires only $m=c_\epsilon K\log \frac{c_1N}{K}$ tests to recover $(1-\epsilon)$ fraction of the defective items with high probability (w.h.p) i.e., with probability approaching $1$ asymptotically in $N$ and $K$, where the value of constants $c_\epsilon$ and $\ell$ are a function of the desired error floor $\epsilon$ and constant $c_1=\frac{\ell}{c_\epsilon}$ (observed to be approximately equal to 1 for various values of $\epsilon$). More importantly the iterative decoding algorithm has a sub-linear computational complexity of $\mathcal{O}(K\log \frac{N}{K})$ which is known to be optimal. Also for $m=c_2 K\log K\log \frac{N}{K}$ tests our scheme recovers the \textit{whole} set of defective items w.h.p. These results are valid for both noiseless and noisy versions of the problem as long as the number of defective items scale sub-linearly with the total number of items, i.e., $K=o(N)$. The simulation results validate the theoretical results by showing a substantial improvement in the number of tests required when compared to the testing scheme based on left-regular sparse-graphs.

Connected through hyperedge HE-1 (induced by source paper "sublinear time non adaptive group testing with o k log n tests via bit mixing coding"):
  with members:
    - "optimal non adaptive group testing"
    - "group testing an information theory perspective"
    - "simple codes and sparse recovery with fast decoding"
    - "performance of group testing algorithms with near constant tests per item"
    - "information theoretic and algorithmic thresholds for group testing"
    - "cross sender bit mixing coding"
    - "on the optimality of the kautz singleton construction in probabilistic group testing"
    - "near optimal noisy group testing via separate decoding of items"

Connected through hyperedge HE-2 (induced by source paper "sparse graph codes for non adaptive quantitative group testing"):
  with members:
    - "a simple and efficient strategy for the coin weighing problem with a spring scale"
    - "a fast noniterative algorithm for compressive sensing using binary measurement matrices"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "sub linear time support recovery for compressed sensing using sparse graph codes"
    - "optimal nested test plan for combinatorial quantitative group testing"
    - "compressed sensing with sparse binary matrices instance optimal error guarantees in near optimal time"

Connected through hyperedge HE-3 (induced by source paper "cross sender bit mixing coding"):
  with members:
    - "non adaptive group testing framework based on concatenation code"
    - "nonadaptive group testing with random set of defectives"
    - "group testing schemes from codes and designs"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "computing in additive networks with bounded information codes"
    - "group testing algorithms bounds and simulations"
    - "on almost disjunct matrices for group testing"
    - "bounds on contention management in radio networks"

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)