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: "semi supervised ordinal regression based on empirical risk minimization"
  Abstract: We consider the semi-supervised ordinal regression problem, where unlabeled data are given in addition to ordinal labeled data. There are many evaluation metrics in ordinal regression such as the mean absolute error, mean squared error, and mean classification error. Existing work does not take the evaluation metric into account, has a restriction on the model choice, and has no theoretical guarantee. To mitigate these problems, we propose a method based on the empirical risk minimization (ERM) framework that is applicable to optimizing all of the metrics mentioned above. Also, our method has flexible choices of models, surrogate losses, and optimization algorithms. Moreover, our method does not require a restrictive assumption on unlabeled data such as the cluster assumption and manifold assumption. We provide an estimation error bound to show that our learning method is consistent. Finally, we conduct experiments to show the usefulness of our framework.

Connected through hyperedge HE-1 (induced by source paper "quadruply stochastic gradient method for large scale nonlinear semi supervised ordinal regression auc optimization"):
  with members:
    - "quadruply stochastic gradients for large scale nonlinear semi supervised auc optimization"
    - "scalable semi supervised svm via triply stochastic gradients"
    - "scalable kernel methods via doubly stochastic gradients"
    - "one pass auc optimization"
    - "on the consistency of auc pairwise optimization"

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)