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: "learning using local membership queries"
  Abstract: We introduce a new model of membership query (MQ) learning, where the learning algorithm is restricted to query points that are \emph{close} to random examples drawn from the underlying distribution. The learning model is intermediate between the PAC model (Valiant, 1984) and the PAC+MQ model (where the queries are allowed to be arbitrary points). #R##N#Membership query algorithms are not popular among machine learning practitioners. Apart from the obvious difficulty of adaptively querying labelers, it has also been observed that querying \emph{unnatural} points leads to increased noise from human labelers (Lang and Baum, 1992). This motivates our study of learning algorithms that make queries that are close to examples generated from the data distribution. #R##N#We restrict our attention to functions defined on the $n$-dimensional Boolean hypercube and say that a membership query is local if its Hamming distance from some example in the (random) training data is at most $O(\log(n))$. We show the following results in this model: #R##N#(i) The class of sparse polynomials (with coefficients in R) over $\{0,1\}^n$ is polynomial time learnable under a large class of \emph{locally smooth} distributions using $O(\log(n))$-local queries. This class also includes the class of $O(\log(n))$-depth decision trees. #R##N#(ii) The class of polynomial-sized decision trees is polynomial time learnable under product distributions using $O(\log(n))$-local queries. #R##N#(iii) The class of polynomial size DNF formulas is learnable under the uniform distribution using $O(\log(n))$-local queries in time $n^{O(\log(\log(n)))}$. #R##N#(iv) In addition we prove a number of results relating the proposed model to the traditional PAC model and the PAC+MQ model.

Connected through hyperedge HE-1 (induced by source paper "on the hardness of robust classification"):
  with members:
    - "a formalization of robustness for deep neural networks"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial vulnerability for any classifier"
    - "adversarial examples from cryptographic pseudo random generators"
    - "adversarial risk and robustness general definitions and implications for the uniform distribution"
    - "are adversarial examples inevitable"
    - "robustness of classifiers from adversarial to random noise"
    - "analysis of classifiers robustness to adversarial perturbations"

Connected through hyperedge HE-2 (induced by source paper "exploring connections between active learning and model extraction"):
  with members:
    - "adversarial examples that fool both computer vision and time limited humans"
    - "sok security and privacy in machine learning"
    - "stealing hyperparameters in machine learning"
    - "robust physical world attacks on deep learning models"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"
    - "practical black box attacks against machine learning"
    - "near optimal active learning of halfspaces via query synthesis in the noisy setting"
    - "stealing machine learning models via prediction apis"

Connected through hyperedge HE-3 (induced by source paper "active classification with comparison queries"):
  with members:
    - "interactive bayesian hierarchical clustering"
    - "dimension coupling optimal active learning of halfspaces via query synthesis"
    - "clustering with same cluster queries"
    - "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"
    - "efficient active learning of halfspaces an aggressive approach"

Connected through hyperedge HE-4 (induced by source paper "distribution free learning with local queries"):
  with members:
    - "learning using 1 local membership queries"
    - "complexity theoretic limitations on learning dnf s"

Connected through hyperedge HE-5 (induced by source paper "learning using 1 local membership queries"):
  with members:
    - "complexity theoretic limitations on learning dnf s"
    - "from average case complexity to improper learning complexity"
    - "more data speeds up training time in learning halfspaces over sparse vectors"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

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)