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: "distribution specific agnostic boosting"
  Abstract: We consider the problem of boosting the accuracy of weak learning algorithms in the agnostic learning framework of Haussler (1992) and Kearns et al. (1992). Known algorithms for this problem (Ben-David et al., 2001; Gavinsky, 2002; Kalai et al., 2008) follow the same strategy as boosting algorithms in the PAC model: the weak learner is executed on the same target function but over different distributions on the domain. We demonstrate boosting algorithms for the agnostic learning framework that only modify the distribution on the labels of the points (or, equivalently, modify the target function). This allows boosting a distribution-specific weak agnostic learner to a strong agnostic learner with respect to the same distribution. #R##N#When applied to the weak agnostic parity learning algorithm of Goldreich and Levin (1989) our algorithm yields a simple PAC learning algorithm for DNF and an agnostic learning algorithm for decision trees over the uniform distribution using membership queries. These results substantially simplify Jackson's famous DNF learning algorithm (1994) and the recent result of Gopalan et al. (2008). #R##N#We also strengthen the connection to hard-core set constructions discovered by Klivans and Servedio (1999) by demonstrating that hard-core set constructions that achieve the optimal hard-core set size (given by Holenstein (2005) and Barak et al. (2009)) imply distribution-specific agnostic boosting algorithms. Conversely, our boosting algorithm gives a simple hard-core set construction with an (almost) optimal hard-core set size.

Connected through hyperedge HE-1 (induced by source paper "online agnostic boosting via regret minimization"):
  with members:
    - "boosting for dynamical systems"
    - "passing tests without memorizing two models for fooling discriminators"
    - "introduction to online convex optimization"
    - "online control with adversarial disturbances"
    - "the role of interactivity in local differential privacy"
    - "private learning implies online learning an efficient reduction"
    - "logarithmic regret for online control"
    - "the nonstochastic control problem"

Connected through hyperedge HE-2 (induced by source paper "adversarially robust low dimensional representations"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "how hard is robust mean estimation"
    - "optimal average case reductions to sparse pca from weak assumptions to strong hardness"
    - "on robustness to adversarial examples and polynomial optimization"
    - "approximating operator norms via generalized krivine rounding"
    - "a spectral view of adversarially robust features"
    - "high dimensional robust mean estimation in nearly linear time"
    - "high accuracy low precision training"

Connected through hyperedge HE-3 (induced by source paper "multiaccuracy black box post processing for fairness in classification"):
  with members:
    - "detecting and correcting for label shift with black box predictors"
    - "fairness through computationally bounded awareness"
    - "on fairness and calibration"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "algorithmic decision making and the cost of fairness"
    - "calibration for the computationally identifiable masses"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-4 (induced by source paper "agnostic learning of disjunctions on symmetric distributions"):
  with members:
    - "approximate resilience monotonicity and the complexity of agnostic learning"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning coverage functions and private release of marginals"
    - "agnostic learning of monomials by halfspaces is hard"
    - "a complete characterization of statistical query learning with applications to evolvability"
    - "privately releasing conjunctions and the statistical query barrier"

Connected through hyperedge HE-5 (induced by source paper "representation approximation and learning of submodular functions using low rank decision trees"):
  with members:
    - "learning pseudo boolean k dnf and submodular functions"
    - "submodular functions are noise stable"
    - "private data release via learning thresholds"
    - "learning valuation functions"
    - "from convex optimization to randomized mechanisms toward optimal combinatorial auctions"
    - "a note on concentration of submodular functions"
    - "submodular functions learnability structure and optimization"
    - "a complete characterization of statistical query learning with applications to evolvability"

Connected through hyperedge HE-6 (induced by source paper "nearly optimal solutions for the chow parameters problem and low weight approximation of halfspaces"):
  with members:
    - "learning dnf expressions from fourier spectrum"
    - "the inverse shapley value problem"
    - "agnostic learning of monomials by halfspaces is hard"
    - "improved approximation of linear threshold functions"
    - "bounded independence fools halfspaces"

Connected through hyperedge HE-7 (induced by source paper "a complete characterization of statistical query learning with applications to evolvability"):
  with members:
    - "distribution independent evolvability of linear threshold functions"
    - "evolution with drifting targets"
    - "what can we learn privately"
    - "noise tolerant learning the parity problem and the statistical query model"

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)