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: "surrogate regret bounds for bipartite ranking via strongly proper losses"
  Abstract: The problem of bipartite ranking, where instances are labeled positive or negative and the goal is to learn a scoring function that minimizes the probability of mis-ranking a pair of positive and negative instances (or equivalently, that maximizes the area under the ROC curve), has been widely studied in recent years. A dominant theoretical and algorithmic framework for the problem has been to reduce bipartite ranking to pairwise classification; in particular, it is well known that the bipartite ranking regret can be formulated as a pairwise classification regret, which in turn can be upper bounded using usual regret bounds for classification problems. Recently, Kotlowski et al. (2011) showed regret bounds for bipartite ranking in terms of the regret associated with balanced versions of the standard (non-pairwise) logistic and exponential losses. In this paper, we show that such (non-pairwise) surrogate regret bounds for bipartite ranking can be obtained in terms of a broad class of proper (composite) losses that we term as strongly proper. Our proof technique is much simpler than that of Kotlowski et al. (2011), and relies on properties of proper (composite) losses as elucidated recently by Reid and Williamson (2010, 2011) and others. Our result yields explicit surrogate bounds (with no hidden balancing terms) in terms of a variety of strongly proper losses, including for example logistic, exponential, squared and squared hinge losses as special cases. We also obtain tighter surrogate bounds under certain low-noise conditions via a recent result of Clemencon and Robbiano (2011).

Connected through hyperedge HE-1 (induced by source paper "risk bounds for low cost bipartite ranking"):
  with members:
    - "one pass auc optimization"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "on the consistency of auc pairwise optimization"

Connected through hyperedge HE-2 (induced by source paper "consistency and finite sample behavior of binary class probability estimation"):
  with members:
    - "fast rates with unbounded losses"
    - "fast rates for general unbounded loss functions from erm to generalized bayes"
    - "convex risk minimization and conditional probability estimation"

Connected through hyperedge HE-3 (induced by source paper "a no regret generalization of hierarchical softmax to extreme multi label classification"):
  with members:
    - "logarithmic time online multiclass prediction"
    - "fast label embeddings via randomized linear algebra"
    - "conditional probability tree estimation analysis and algorithms"
    - "deep networks with large output spaces"
    - "distributed representations of words and phrases and their compositionality"
    - "optimizing f measure a tale of two approaches"
    - "error correcting tournaments"

Connected through hyperedge HE-4 (induced by source paper "on structured prediction theory with calibrated convex surrogate losses"):
  with members:
    - "a consistent regularization approach for structured prediction"
    - "training deep neural networks via direct loss minimization"
    - "simultaneous model selection and optimization through parameter free stochastic learning"
    - "on the consistency of ordinal regression methods"
    - "convex calibration dimension for multiclass loss matrices"

Connected through hyperedge HE-5 (induced by source paper "regret bounds for non decomposable metrics with missing labels"):
  with members:
    - "robust regression via hard thresholding"
    - "surrogate regret bounds for generalized classification performance metrics"
    - "pu learning for matrix completion"
    - "provable inductive matrix completion"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-6 (induced by source paper "learning from binary labels with instance dependent corruption"):
  with members:
    - "loss factorization weakly supervised learning and label noise robustness"
    - "learning with symmetric label noise the importance of being unhinged"
    - "efficient learning of linear separators under bounded noise"
    - "noise tolerance under risk minimization"

Connected through hyperedge HE-7 (induced by source paper "learning reductions that really work"):
  with members:
    - "learning to search better than your teacher"
    - "logarithmic time online multiclass prediction"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "efficient programmable learning to search"
    - "least squares revisited scalable approaches for multi class prediction"
    - "normalized online learning"
    - "output space search for structured prediction"
    - "online importance weight aware updates"

Connected through hyperedge HE-8 (induced by source paper "surrogate regret bounds for generalized classification performance metrics"):
  with members:
    - "optimizing f measure a tale of two approaches"

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)