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: "active property testing"
  Abstract: One of the motivations for property testing of boolean functions is the idea that testing can serve as a preprocessing step before learning. However, in most machine learning applications, it is not possible to request for labels of fictitious examples constructed by the algorithm. Instead, the dominant query paradigm in applied machine learning, called active learning, is one where the algorithm may query for labels, but only on points in a given polynomial-sized (unlabeled) sample, drawn from some underlying distribution D. In this work, we bring this well-studied model in learning to the domain of testing. #R##N#We show that for a number of important properties, testing can still yield substantial benefits in this setting. This includes testing unions of intervals, testing linear separators, and testing various assumptions used in semi-supervised learning. In addition to these specific results, we also develop a general notion of the testing dimension of a given property with respect to a given distribution. We show this dimension characterizes (up to constant factors) the intrinsic number of label requests needed to test that property. We develop such notions for both the active and passive testing models. We then use these dimensions to prove a number of lower bounds, including for linear separators and the class of dictator functions. #R##N#Our results show that testing can be a powerful tool in realistic models for learning, and further that active testing exhibits an interesting and rich structure. Our work in addition brings together tools from a range of areas including U-statistics, noise-sensitivity, self-correction, and spectral analysis of random matrices, and develops new tools that may be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "improvability through semi supervised learning a survey of theoretical results"):
  with members:
    - "the information theoretic value of unlabeled data in semi supervised learning"
    - "when can unlabeled data improve the learning rate"
    - "a distribution dependent and independent complexity analysis of manifold regularization"
    - "almost everywhere algorithmic stability and generalization error"
    - "on causal and anticausal learning"
    - "transductive rademacher complexity and its applications"
    - "explicit learning curves for transduction and application to clustering and compression algorithms"

Connected through hyperedge HE-2 (induced by source paper "distribution free testing of linear functions on r n"):
  with members:
    - "almost optimal distribution free junta testing"
    - "is your function low dimensional"
    - "testing halfspaces over rotation invariant distributions"
    - "sample based high dimensional convexity testing"
    - "testing properties of functions on finite groups"
    - "tight bounds for the distribution free testing of monotone conjunctions"
    - "partially symmetric functions are efficiently isomorphism testable"

Connected through hyperedge HE-3 (induced by source paper "approximating the noise sensitivity of a monotone boolean function"):
  with members:
    - "a polynomial lower bound for testing monotonicity"
    - "analysis of boolean functions"
    - "new algorithms and lower bounds for monotonicity testing"
    - "the average sensitivity of an intersection of half spaces"
    - "submodular functions are noise stable"
    - "average sensitivity and noise sensitivity of polynomial threshold functions"

Connected through hyperedge HE-4 (induced by source paper "estimating learnability in the sublinear data regime"):
  with members:
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "spectrum estimation from samples"
    - "minimax estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "new algorithms and lower bounds for monotonicity testing"
    - "the power of localization for efficiently learning linear separators with noise"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator"

Connected through hyperedge HE-5 (induced by source paper "is your function low dimensional"):
  with members:
    - "distribution free junta testing"
    - "learning geometric concepts with nasty noise"
    - "sample based high dimensional convexity testing"
    - "settling the query complexity of non adaptive junta testing"
    - "active and passive learning of linear separators under log concave distributions"

Connected through hyperedge HE-6 (induced by source paper "sample based high dimensional convexity testing"):
  with members:
    - "on active and passive testing"
    - "optimal testing for properties of distributions"

Connected through hyperedge HE-7 (induced by source paper "testing piecewise functions"):
  with members:
    - "optimal unateness testers for real valued functions adaptivity helps"
    - "the optimal sample complexity of pac learning"

Connected through hyperedge HE-8 (induced by source paper "testing k monotonicity"):
  with members:
    - "sensitivity conjecture and log rank conjecture for functions with small alternating numbers"
    - "analysis of boolean functions"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "new algorithms and lower bounds for monotonicity testing"
    - "learning circuits with few negations"
    - "an optimal lower bound for monotonicity testing over hypergrids"
    - "a o n monotonicity tester for boolean functions over the hypercube"
    - "optimal bounds for monotonicity and lipschitz testing over hypercubes and hypergrids"

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)