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: "efficient nonrandom construction and decoding of non adaptive group testing"
  Abstract: The task of non-adaptive group testing is to identify up to $d$ defective items from $N$ items, where a test is positive if it contains at least one defective item, and negative otherwise. If there are $t$ tests, they can be represented as a $t \times N$ measurement matrix. We have answered the question of whether there exists a scheme such that a larger measurement matrix, built from a given $t\times N$ measurement matrix, can be used to identify up to $d$ defective items in time $O(t \log_2{N})$. In the meantime, a $t \times N$ nonrandom measurement matrix with $t = O \left(\frac{d^2 \log_2^2{N}}{(\log_2(d\log_2{N}) - \log_2{\log_2(d\log_2{N})})^2} \right)$ can be obtained to identify up to $d$ defective items in time $\mathrm{poly}(t)$. This is much better than the best well-known bound, $t = O \left( d^2 \log_2^2{N} \right)$. For the special case $d = 2$, there exists an efficient nonrandom construction in which at most two defective items can be identified in time $4\log_2^2{N}$ using $t = 4\log_2^2{N}$ tests. Numerical results show that our proposed scheme is more practical than existing ones, and experimental results confirm our theoretical analysis. In particular, up to $2^{7} = 128$ defective items can be identified in less than $16$s even for $N = 2^{100}$.

Connected through hyperedge HE-1 (induced by source paper "serial quantization for sparse time sequences"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "information theoretic and algorithmic thresholds for group testing"
    - "efficient data collection over multiple access wireless sensors network"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "distributed quantization for measurement of correlated sparse sources over noisy channels"
    - "distributed compressed sensing for static and time varying networks"
    - "spatial compressive sensing for mimo radar"

Connected through hyperedge HE-2 (induced by source paper "improved encoding and decoding for non adaptive threshold group testing"):
  with members:
    - "a framework for generalized group testing with inhibitors and its potential application in neuroscience"
    - "separable codes for the symmetric multiple access channel"
    - "superimposed codes and threshold group testing"
    - "grotesque noisy group testing quick and efficient"
    - "improved constructions for non adaptive threshold group testing"
    - "noise resilient group testing limitations and constructions"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-3 (induced by source paper "efficient and error tolerant schemes for non adaptive complex group testing and its application in complex disease genetics"):
  with members:
    - "a framework for generalized group testing with inhibitors and its potential application in neuroscience"
    - "grotesque noisy group testing quick and efficient"
    - "improved constructions for non adaptive threshold group testing"
    - "noise resilient group testing limitations and constructions"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-4 (induced by source paper "a framework for generalized group testing with inhibitors and its potential application in neuroscience"):
  with members:
    - "improved encoding and decoding for non adaptive threshold group testing"
    - "sublinear decoding schemes for non adaptive group testing with inhibitors"
    - "computational tradeoffs in biological neural networks self stabilizing winner take all networks"
    - "non adaptive group testing with inhibitors"
    - "near optimal stochastic threshold group testing"
    - "improved constructions for non adaptive threshold group testing"
    - "graph constrained group testing"
    - "noise resilient group testing limitations and constructions"

Connected through hyperedge HE-5 (induced by source paper "sublinear decoding schemes for non adaptive group testing with inhibitors"):
  with members:
    - "a framework for generalized group testing with inhibitors and its potential application in neuroscience"
    - "separable codes for the symmetric multiple access channel"
    - "efficiently decodable non adaptive threshold group testing"
    - "non adaptive group testing with inhibitors"
    - "boolean compressed sensing and noisy group testing"
    - "improved constructions for non adaptive threshold group testing"
    - "noise resilient group testing limitations and constructions"

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)