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 "classification from pairwise similarity and unlabeled data"):
  Abstract: One of the biggest bottlenecks in supervised learning is its high labeling cost. To overcome this problem, we propose a new weakly-supervised learning setting called SU classification, where only similar (S) data pairs (two examples belong to the same class) and unlabeled (U) data are needed, instead of fully-supervised data. We show that an unbiased estimator of the classification risk can be obtained only from SU data, and its empirical risk minimizer achieves the optimal parametric convergence rate. Finally, we demonstrate the effectiveness of the proposed method through experiments.

Includes members:
  Member "positive unlabeled learning with non negative risk estimator":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "object detection as a positive unlabeled problem")
  Member "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "convex formulation of multiple instance learning from positive and unlabeled bags":
    also connected through other hyperedges:
      (none)
  Member "learning from video and text via large scale discriminative clustering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "end to end learning of visual representations from uncurated instructional videos")
  Member "theoretical comparisons of positive unlabeled learning against positive negative learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "combating noisy labels by agreement a joint training method with co regularization")
  Member "mixture proportion estimation via kernel embedding of distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards mixture proportion estimation without irreducibility")
  Member "semi supervised information maximization clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semi supervised cross entropy clustering with information bottleneck constraint")
  Member "information theoretic semi supervised metric learning via entropy regularization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "affinity guided geometric semi supervised metric learning")

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)