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: "derandomization and group testing"
  Abstract: The rapid development of derandomization theory, which is a fundamental area in theoretical computer science, has recently led to many surprising applications outside its initial intention. We will review some recent such developments related to combinatorial group testing. In its most basic setting, the aim of group testing is to identify a set of "positive" individuals in a population of items by taking groups of items and asking whether there is a positive in each group. #R##N#In particular, we will discuss explicit constructions of optimal or nearly-optimal group testing schemes using "randomness-conducting" functions. Among such developments are constructions of error-correcting group testing schemes using randomness extractors and condensers, as well as threshold group testing schemes from lossless condensers.

Connected through hyperedge HE-1 (induced by source paper "group testing an information theory perspective"):
  with members:
    - "quantitative group testing in the sublinear regime"
    - "understanding phase transitions via mutual information and mmse"
    - "performance of group testing algorithms with near constant tests per item"
    - "learning erd h o s r enyi random graphs via edge detecting queries"
    - "information theoretic and algorithmic thresholds for group testing"
    - "novel impossibility results for group testing"
    - "noisy adaptive group testing bounds and algorithms"
    - "near optimal noisy group testing via separate decoding of items"

Connected through hyperedge HE-2 (induced by source paper "exact learning from an honest teacher that answers membership queries"):
  with members:
    - "exact learning of juntas from membership queries"
    - "almost optimal cover free families"
    - "progress on polynomial identity testing ii"
    - "linear time constructions of some d restriction problems"
    - "on exact learning monotone dnf from membership queries"
    - "learning boolean halfspaces with small weights from membership queries"
    - "bounds on the rate of disjunctive codes"
    - "efficient computation of representative sets with applications in parameterized and exact algorithms"

Connected through hyperedge HE-3 (induced by source paper "learning mixtures of sparse linear regressions using sparse graph codes"):
  with members:
    - "solving a mixture of many random linear equations by tensor decomposition and alternating minimization"
    - "nonadaptive group testing with random set of defectives"
    - "fast and robust compressive phase retrieval with sparse graph codes"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "sub linear time support recovery for compressed sensing using sparse graph codes"
    - "guess who rated this movie identifying users through subspace clustering"
    - "learning mixtures of linear classifiers"

Connected through hyperedge HE-4 (induced by source paper "quantitative group testing for heavy hitter detection"):
  with members:
    - "probabilistic group testing under sum observations a parallelizable 2 approximation for entropy loss"
    - "group testing algorithms bounds and simulations"
    - "semiquantitative group testing"
    - "near optimal adaptive compressed sensing"
    - "boolean compressed sensing and noisy group testing"
    - "group testing with probabilistic tests theory design and application"
    - "graph constrained group testing"

Connected through hyperedge HE-5 (induced by source paper "optimal nested test plan for combinatorial quantitative group testing"):
  with members:
    - "group testing algorithms bounds and simulations"
    - "semiquantitative group testing"
    - "near optimal adaptive compressed sensing"
    - "group testing with probabilistic tests theory design and application"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-6 (induced by source paper "neighbor discovery for wireless networks via compressed sensing"):
  with members:
    - "boolean compressed sensing and noisy group testing"
    - "graph constrained group testing"
    - "compressed sensing with probabilistic measurements a group testing solution"
    - "neighbor discovery in wireless networks a multiuser detection approach"
    - "list decoding of noisy reed muller like codes"

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)