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: "efficiently decodable non adaptive threshold group testing"
  Abstract: We consider non-adaptive threshold group testing for identification of up to $d$ defective items in a set of $N$ items, where a test is positive if it contains at least $2 \leq u \leq d$ defective items, and negative otherwise. The defective items can be identified using $t = O \left( \frac{d^2}{d^2 - u^2} \left( \frac{\mathrm{e} (d - u)}{u} \right)^u \left(u \log{\frac{d}{u}} + \log{\frac{1}{\epsilon}} \right) \cdot d^2 \log{N} \right)$ tests with probability at least $1 - \epsilon$ for any $\epsilon > 0$ or $t = O \left( \frac{d^2}{d^2 - u^2} \left( \frac{\mathrm{e} (d-u)}{u} \right)^u \cdot \left( d\log{\frac{N}{d}} + u\log{\frac{d}{u}} \right) \cdot d^2 \log{N} \right)$ tests with probability 1. The decoding time is $\frac{t}{O(d^2 \log{N})} \times \mathrm{poly}(d^2 \log{N})$. This result significantly improves the best known results for decoding non-adaptive threshold group testing: $O(N\log{N} + N \log{\frac{1}{\epsilon}})$ for probabilistic decoding, where $\epsilon > 0$, and $O(N^u \log{N})$ for deterministic decoding.

Connected through hyperedge HE-1 (induced by source paper "improved non adaptive algorithms for threshold group testing with a gap"):
  with members:
    - "group testing an information theory perspective"
    - "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"

Connected through hyperedge HE-2 (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"
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "separable codes for the symmetric multiple access channel"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "efficient nonrandom construction and decoding of non adaptive group testing"):
  with members:
    - "a framework for generalized group testing with inhibitors and its potential application in neuroscience"
    - "efficient decoding schemes for noisy non adaptive group testing when noise depends on number of items in test"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "faster algorithms for multivariate interpolation with multiplicities and simultaneous polynomial approximations"
    - "boolean compressed sensing and noisy group testing"
    - "noise resilient group testing limitations and constructions"
    - "explicit non adaptive combinatorial group testing schemes"

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)