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 "learn to expect the unexpected probably approximately correct domain generalization"):
  Abstract: Domain generalization is the problem of machine learning when the training data and the test data come from different data domains. We present a simple theoretical model of learning to generalize across domains in which there is a meta-distribution over data distributions, and those data distributions may even have different supports. In our model, the training data given to a learning algorithm consists of multiple datasets each from a single domain drawn in turn from the meta-distribution. We study this model in three different problem settings---a multi-domain Massart noise setting, a decision tree multi-dataset setting, and a feature selection setting, and find that computationally efficient, polynomial-sample domain generalization is possible in each. Experiments demonstrate that our feature selection algorithm indeed ignores spurious correlations and improves generalization.

Includes members:
  Member "episodic training for domain generalization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "domain adaptive ensemble learning")
  Member "domain generalization by solving jigsaw puzzles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "domain adaptive ensemble learning")
  Member "learning robust representations by projecting superficial statistics out":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "out of distribution generalization via risk extrapolation rex")
  Member "domain generalization via model agnostic learning of semantic features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "domain adaptive ensemble learning")
  Member "distribution independent pac learning of halfspaces with massart noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "best sources forward domain generalization through source specific nets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards a general model of knowledge for facial analysis by multi source transfer learning")
  Member "multi source domain adaptation with mixture of experts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "generalizing to unseen domains via adversarial data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cross domain few shot classification via learned feature wise transformation")

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)