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: "explicit non adaptive combinatorial group testing schemes"
  Abstract: Group testing is a long studied problem in combinatorics: A small set of $r$ ill people should be identified out of the whole ($n$ people) by using only queries (tests) of the form "Does set X contain an ill human?". In this paper we provide an explicit construction of a testing scheme which is better (smaller) than any known explicit construction. This scheme has $\bigT{\min[r^2 \ln n,n]}$ tests which is as many as the best non-explicit schemes have. In our construction we use a fact that may have a value by its own right: Linear error-correction codes with parameters $[m,k,\delta m]_q$ meeting the Gilbert-Varshamov bound may be constructed quite efficiently, in $\bigT{q^km}$ time.

Connected through hyperedge HE-1 (induced by source paper "group testing with runlength constraints for topological molecular storage"):
  with members:
    - "quantitative group testing in the sublinear regime"
    - "non adaptive quantitative group testing using irregular sparse graph codes"
    - "group testing an information theory perspective"
    - "performance of group testing algorithms with near constant tests per item"
    - "the capacity of bernoulli nonadaptive group testing"
    - "sparse combinatorial group testing"
    - "decoding from pooled data phase transitions of message passing"
    - "nearly optimal sparse group testing"

Connected through hyperedge HE-2 (induced by source paper "deterministic sparse fourier transform with an ell_infty guarantee"):
  with members:
    - "dimension independent sparse fourier transform"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "deterministic heavy hitters with sublinear query time"
    - "an adaptive sublinear time block sparse fourier transform"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "fourier sparse interpolation without a frequency gap"
    - "a robust sparse fourier transform in the continuous setting"
    - "the restricted isometry property of subsampled fourier matrices"

Connected through hyperedge HE-3 (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"
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "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-4 (induced by source paper "simple codes and sparse recovery with fast decoding"):
  with members:
    - "nearly optimal deterministic algorithm for sparse walsh hadamard transform"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "for all sparse recovery in near optimal time"
    - "noise resilient group testing limitations and constructions"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

Connected through hyperedge HE-5 (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"
    - "efficient nonrandom construction and decoding of non adaptive 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-6 (induced by source paper "on the optimality of the kautz singleton construction in probabilistic group testing"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "noisy non adaptive group testing a near definite defectives approach"
    - "near optimal noisy group testing via separate decoding of items"
    - "nearly optimal sparse group testing"
    - "nonadaptive group testing with random set of defectives"
    - "learning immune defectives graph through group tests"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "group testing algorithms bounds and simulations"

Connected through hyperedge HE-7 (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"
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "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"

Connected through hyperedge HE-8 (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"
    - "efficiently decodable non adaptive threshold group testing"
    - "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"

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.DS(Data Structures and Algorithms)