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 learning using smooth relative regret approximations with applications"
  Abstract: The disagreement coefficient of Hanneke has become a central data independent invariant in proving active learning rates. It has been shown in various ways that a concept class with low complexity together with a bound on the disagreement coefficient at an optimal solution allows active learning rates that are superior to passive learning ones. #R##N#We present a different tool for pool based active learning which follows from the existence of a certain uniform version of low disagreement coefficient, but is not equivalent to it. In fact, we present two fundamental active learning problems of significant interest for which our approach allows nontrivial active learning bounds. However, any general purpose method relying on the disagreement coefficient bounds only fails to guarantee any useful bounds for these problems. #R##N#The tool we use is based on the learner's ability to compute an estimator of the difference between the loss of any hypotheses and some fixed "pivotal" hypothesis to within an absolute error of at most $\eps$ times the

Connected through hyperedge HE-1 (induced by source paper "revisiting perceptron efficient and label optimal learning of halfspaces"):
  with members:
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "near optimal active learning of halfspaces via query synthesis in the noisy setting"
    - "efficient and parsimonious agnostic active learning"
    - "complexity theoretic limitations on learning halfspaces"
    - "active and passive learning of linear separators under log concave distributions"
    - "agnostic active learning without constraints"

Connected through hyperedge HE-2 (induced by source paper "refined error bounds for several learning algorithms"):
  with members:
    - "the optimal sample complexity of pac learning"
    - "beyond disagreement based agnostic active learning"
    - "a compression technique for analyzing disagreement based active learning"
    - "minimax analysis of active learning"
    - "active and passive learning of linear separators under log concave distributions"

Connected through hyperedge HE-3 (induced by source paper "the utility of abstaining in binary classification"):
  with members:
    - "exploring compact reinforcement learning representations with linear regression"
    - "agnostic active learning without constraints"
    - "importance weighted active learning"

Connected through hyperedge HE-4 (induced by source paper "minimax analysis of active learning"):
  with members:
    - "a compression technique for analyzing disagreement based active learning"
    - "the power of localization for efficiently learning linear separators with noise"
    - "active and passive learning of linear separators under log concave distributions"
    - "robust interactive learning"
    - "agnostic active learning without constraints"
    - "importance weighted active learning"

Connected through hyperedge HE-5 (induced by source paper "reducing dueling bandits to cardinal bandits"):
  with members:
    - "decoupling exploration and exploitation in multi armed bandits"
    - "an adaptive algorithm for finite stochastic partial monitoring"

Connected through hyperedge HE-6 (induced by source paper "semantic sort a supervised approach to personalized semantic relatedness"):
  with members:
    - "roget s thesaurus and semantic similarity"
    - "a semantic relatedness measure based on combined encyclopedic ontological and collocational knowledge"
    - "text relatedness based on a word thesaurus"
    - "wikipedia based semantic interpretation for natural language processing"
    - "the google similarity distance"
    - "roget s thesaurus as a lexical resource for natural language processing"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-7 (induced by source paper "breaking the small cluster barrier of graph clustering"):
  with members:
    - "efficient active algorithms for hierarchical clustering"
    - "clustering partially observed graphs via convex optimization"
    - "active clustering robust and efficient hierarchical clustering using adaptively selected similarities"
    - "nuclear norm minimization for the planted clique and biclique problems"

Connected through hyperedge HE-8 (induced by source paper "local correlation clustering"):
  with members:
    - "fast local computation algorithms"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "linear time approximation schemes for the gale berlekamp game and related minimization problems"

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)