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 "unsupervised domain adaptation based on source guided discrepancy"):
  Abstract: Unsupervised domain adaptation is the problem setting where data generating distributions in the source and target domains are different, and labels in the target domain are unavailable. One important question in unsupervised domain adaptation is how to measure the difference between the source and target domains. A previously proposed discrepancy that does not use the source domain labels requires high computational cost to estimate and may lead to a loose generalization error bound in the target domain. To mitigate these problems, we propose a novel discrepancy called source-guided discrepancy (S-disc), which exploits labels in the source domain. As a consequence, S-disc can be computed efficiently with a finite sample convergence guarantee. In addition, we show that S-disc can provide a tighter generalization error bound than the one based on an existing discrepancy. Finally, we report experimental results that demonstrate the advantages of S-disc over the existing discrepancies.

Includes members:
  Member "multiple source domain adaptation with adversarial training of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptation across extreme variations using unlabeled domain bridges")
  Member "asymmetric tri training for unsupervised domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised domain adaptive object detection using forward backward cyclic adaptation")
  Member "adaptation algorithm and theory based on generalized discrepancy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "generalization bounds for domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "new analysis and algorithm for learning with drifting distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "agnostic learning of monomials by halfspaces is hard":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "solving empirical risk minimization in the current matrix multiplication time")
  Member "domain adaptation learning bounds and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "sample selection bias correction theory":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial machine learning perspectives from adversarial risk analysis")

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)