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: "optimal non adaptive group testing"
  Abstract: In non-adaptive group testing we aim to identify a small set of $k\sim n^\theta$ infected individuals out of a population size $n$, $0<\theta<1$. We avail ourselves to a test procedure that can test a group of individuals, with the test rendering a positive result iff at least one individual in the group is infected. All tests are conducted in parallel. The aim is to devise a (possibly randomised) test design with as few tests as possible so that the infected individuals can be identified with high probability. We prove that there occurs a sharp information-theoretic/algorithmic phase transition as the number of tests passes an explicit threshold $m_{\inf}$. Hence, if more than $(1+\epsilon)m_{\inf}$ tests are conducted, then there exist a test design and a polynomial time algorithm that identifies the set of infected individuals with high probability. By contrast, identifying the infected individuals is information-theoretically impossible with fewer than $(1-\epsilon)m_{\inf}$ tests. These results resolve problems prominently posed in [Aldridge et al. 2019, Johnson et al. 2018].

Connected through hyperedge HE-1 (induced by source paper "on the all or nothing behavior of bernoulli group testing"):
  with members:
    - "group testing an information theory perspective"
    - "performance of group testing algorithms with near constant tests per item"
    - "information theoretic and algorithmic thresholds for group testing"
    - "near optimal noisy group testing via separate decoding of items"
    - "the capacity of bernoulli nonadaptive group testing"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "group testing algorithms bounds and simulations"
    - "the capacity of adaptive group testing"

Connected through hyperedge HE-2 (induced by source paper "sublinear time non adaptive group testing with o k log n tests via bit mixing coding"):
  with members:
    - "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"
    - "individual testing is optimal for nonadaptive group testing in the linear regime"

Connected through hyperedge HE-3 (induced by source paper "optimal adaptive group testing"):
  with members:
    - "rates of adaptive group testing in the linear regime"
    - "information theoretic and algorithmic thresholds for group testing"
    - "noisy adaptive group testing bounds and algorithms"
    - "individual testing is optimal for nonadaptive group testing in the linear regime"
    - "the capacity of bernoulli nonadaptive group testing"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "group testing with random pools optimal two stage algorithms"

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.DM(Discrete Mathematics)