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 "probabilistic filtered soft labels for domain adaptation"):
  Abstract: Many domain adaptation (DA) methods aim to project the source and target domains into a common feature space, where the inter-domain distributional differences are reduced and some intra-domain properties preserved. Recent research obtains their respective new representations using some predefined statistics. However, they usually formulate the class-wise statistics using the pseudo hard labels due to no labeled target data, such as class-wise MMD and class scatter matrice. The probabilities of data points belonging to each class given by the hard labels are either 0 or 1, while the soft labels could relax the strong constraint of hard labels and provide a random value between them. Although existing work have noticed the advantage of soft labels, they either deal with thoes class-wise statistics inadequately or introduce those small irrelevant probabilities in soft labels. Therefore, we propose the filtered soft labels to discard thoes confusing probabilities, then both of the class-wise MMD and class scatter matrice are modeled in this way. In order to obtain more accurate filtered soft labels, we take advantage of a well-designed Graph-based Label Propagation (GLP) method, and incorporate it into the DA procedure to formulate a unified framework.

Includes members:
  Member "bridging theory and algorithm for domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "manifold criterion guided transfer learning via intermediate domain generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation")
  Member "discriminative label consistent domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attention aware age agnostic visual place recognition")
  Member "open set domain adaptation by backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "importance weighted adversarial nets for partial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "multi adversarial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "balanced distribution adaptation for transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "impact of imagenet model selection on domain adaptation")
  Member "deep hashing network for unsupervised domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting meta learning as supervised 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)