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: "efficient learning of generalized linear and single index models with isotonic regression"
  Abstract: Generalized Linear Models (GLMs) and Single Index Models (SIMs) provide powerful generalizations of linear regression, where the target variable is assumed to be a (possibly unknown) 1-dimensional function of a linear predictor. In general, these problems entail non-convex estimation procedures, and, in practice, iterative local search heuristics are often used. Kalai and Sastry (2009) recently provided the first provably efficient method for learning SIMs and GLMs, under the assumptions that the data are in fact generated under a GLM and under certain monotonicity and Lipschitz constraints. However, to obtain provable performance, the method requires a fresh sample every iteration. In this paper, we provide algorithms for learning GLMs and SIMs, which are both computationally and statistically efficient. We also provide an empirical study, demonstrating their feasibility in practice.

Connected through hyperedge HE-1 (induced by source paper "efficient second order shape constrained function fitting"):
  with members:
    - "efficient algorithms for non convex isotonic regression through submodular optimization"
    - "fast provable algorithms for isotonic regression in all ell_ p norms"
    - "a deterministic algorithm for fitting a step function to a weighted point set"
    - "lipschitz unimodal and isotonic regression on paths and trees"

Connected through hyperedge HE-2 (induced by source paper "uncovering the dynamics of crowdlearning and the value of knowledge"):
  with members:
    - "shaping social activity by incentivizing users"
    - "modeling adoption and usage of competing products"
    - "engaging with massive online courses"

Connected through hyperedge HE-3 (induced by source paper "online isotonic regression"):
  with members:
    - "large scale probabilistic predictors with and without guarantees of validity"
    - "fast provable algorithms for isotonic regression in all ell_ p norms"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"
    - "predicting accurate probabilities with a ranking loss"

Connected through hyperedge HE-4 (induced by source paper "least squares revisited scalable approaches for multi class prediction"):
  with members:
    - "improving neural networks by preventing co adaptation of feature detectors"

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.AI(Artificial Intelligence)