human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "chainer a deep learning framework for accelerating the research cycle":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scalable and practical natural gradient for large scale deep learning")
  Member "classification from pairwise similarity and unlabeled data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combating noisy labels by agreement a joint training method with co regularization")
  Member "positive unlabeled learning with non negative risk estimator":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object detection as a positive unlabeled problem")
  Member "loss factorization weakly supervised learning and label noise robustness":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "does label smoothing mitigate label noise")
  Member "semi supervised classification based on classification from positive and unlabeled data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "combating noisy labels by agreement a joint training method with co regularization")
  Member "theoretical comparisons of positive unlabeled learning against positive negative learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "combating noisy labels by agreement a joint training method with co regularization")
  Member "on the consistency of ordinal regression methods":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "feature learning to automatically assess radiographic knee osteoarthritis severity")
  Member "transductive ordinal regression":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)