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: "statistical active learning algorithms for noise tolerance and differential privacy"
  Abstract: We describe a framework for designing efficient active learning algorithms that are tolerant to random classification noise and are differentially-private. The framework is based on active learning algorithms that are statistical in the sense that they rely on estimates of expectations of functions of filtered random examples. It builds on the powerful statistical query framework of Kearns (1993). #R##N#We show that any efficient active statistical learning algorithm can be automatically converted to an efficient active learning algorithm which is tolerant to random classification noise as well as other forms of "uncorrelated" noise. The complexity of the resulting algorithms has information-theoretically optimal quadratic dependence on $1/(1-2\eta)$, where $\eta$ is the noise rate. #R##N#We show that commonly studied concept classes including thresholds, rectangles, and linear separators can be efficiently actively learned in our framework. These results combined with our generic conversion lead to the first computationally-efficient algorithms for actively learning some of these concept classes in the presence of random classification noise that provide exponential improvement in the dependence on the error $\epsilon$ over their passive counterparts. In addition, we show that our algorithms can be automatically converted to efficient active differentially-private algorithms. This leads to the first differentially-private active learning algorithms with exponential label savings over the passive case.

Connected through hyperedge HE-1 (induced by source paper "locally private learning without interaction requires separation"):
  with members:
    - "exponential separations in local differential privacy"
    - "the role of interactivity in local differential privacy"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "collecting telemetry data privately"
    - "distributed mean estimation with limited communication"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "the limitations of optimization from samples"

Connected through hyperedge HE-2 (induced by source paper "active anomaly detection via ensembles insights algorithms and interpretability"):
  with members:
    - "active anomaly detection via ensembles"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"
    - "explaining anomalies in groups with characterizing subspace rules"
    - "why should i trust you explaining the predictions of any classifier"
    - "toward supervised anomaly detection"

Connected through hyperedge HE-3 (induced by source paper "private pac learning implies finite littlestone dimension"):
  with members:
    - "model agnostic private learning via stability"
    - "privacy preserving prediction"
    - "adaptive learning with robust generalization guarantees"
    - "simultaneous private learning of multiple concepts"
    - "differentially private release and learning of threshold functions"
    - "algorithmic stability for adaptive data analysis"
    - "the large margin mechanism for differentially private maximization"
    - "characterizing the sample complexity of private learners"

Connected through hyperedge HE-4 (induced by source paper "learning without interaction requires separation"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "distributed mean estimation with limited communication"
    - "algorithmic stability for adaptive data analysis"
    - "preventing false discovery in interactive data analysis is hard"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"
    - "distribution independent evolvability of linear threshold functions"

Connected through hyperedge HE-5 (induced by source paper "active anomaly detection via ensembles"):
  with members:
    - "systematic construction of anomaly detection benchmarks from real data"
    - "toward supervised anomaly detection"

Connected through hyperedge HE-6 (induced by source paper "a general characterization of the statistical query complexity"):
  with members:
    - "on the power of learning from k wise queries"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "preserving statistical validity in adaptive data analysis"
    - "approximate resilience monotonicity and the complexity of agnostic learning"
    - "calibrating data to sensitivity in private data analysis"

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.LG(Machine Learning)