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 to abstain from binary prediction"
  Abstract: We address how to learn a binary classifier capable of abstaining from making a label prediction. Such a classifier hopes to abstain where it would be most inaccurate if forced to predict, so it has two goals in tension with each other: minimizing errors, and avoiding abstaining unnecessarily often. #R##N#In this work, we exactly characterize the best achievable tradeoff between these two goals in a general semi-supervised setting, given an ensemble of classifiers of varying competence as well as unlabeled data on which we wish to predict or abstain. We give an algorithm for learning a classifier which trades off its errors with abstentions in a minimax optimal manner. This algorithm is as efficient as linear learning and prediction, and comes with strong and robust theoretical guarantees. Our analysis extends to a large class of loss functions and other scenarios, including ensembles comprised of "specialist" classifiers that can themselves abstain.

Connected through hyperedge HE-1 (induced by source paper "adversarial examples detection in deep networks with convolutional filter statistics"):
  with members:
    - "energy based generative adversarial network"
    - "improved techniques for training gans"
    - "adversarial examples in the physical world"
    - "foveation based mechanisms alleviate adversarial examples"
    - "towards open set deep networks"
    - "universum prescription regularization using unlabeled data"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "learning with a strong adversary"

Connected through hyperedge HE-2 (induced by source paper "unanimous prediction for 100 precision with application to learning semantic mappings"):
  with members:
    - "compositional semantic parsing on semi structured tables"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"
    - "a joint model of language and perception for grounded attribute learning"
    - "learning dependency based compositional semantics"

Connected through hyperedge HE-3 (induced by source paper "the extended littlestone s dimension for learning with mistakes and abstentions"):
  with members:
    - "distribution independent reliable learning"
    - "beyond disagreement based agnostic active learning"
    - "online learning via sequential complexities"
    - "a tutorial on conformal prediction"

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)