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 "blending target domain adaptation by adversarial meta adaptation networks"):
  Abstract: (Unsupervised) Domain Adaptation (DA) seeks for classifying target instances when solely provided with source labeled and target unlabeled examples for training. Learning domain-invariant features helps to achieve this goal, whereas it underpins unlabeled samples drawn from a single or multiple explicit target domains (Multi-target DA). In this paper, we consider a more realistic transfer scenario: our target domain is comprised of multiple sub-targets implicitly blended with each other, so that learners could not identify which sub-target each unlabeled sample belongs to. This Blending-target Domain Adaptation (BTDA) scenario commonly appears in practice and threatens the validities of most existing DA algorithms, due to the presence of domain gaps and categorical misalignments among these hidden sub-targets. #R##N#To reap the transfer performance gains in this new scenario, we propose Adversarial Meta-Adaptation Network (AMEAN). AMEAN entails two adversarial transfer learning processes. The first is a conventional adversarial transfer to bridge our source and mixed target domains. To circumvent the intra-target category misalignment, the second process presents as ``learning to adapt'': It deploys an unsupervised meta-learner receiving target data and their ongoing feature-learning feedbacks, to discover target clusters as our ``meta-sub-target'' domains. These meta-sub-targets auto-design our meta-sub-target DA loss, which empirically eliminates the implicit category mismatching in our mixed target. We evaluate AMEAN and a variety of DA algorithms in three benchmarks under the BTDA setup. Empirical results show that BTDA is a quite challenging transfer setup for most existing DA algorithms, yet AMEAN significantly outperforms these state-of-the-art baselines and effectively restrains the negative transfer effects in BTDA.

Includes members:
  Member "unbiasing semantic segmentation for robot perception using synthetic data feature transfer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "box level segmentation supervised deep neural networks for accurate and real time multispectral pedestrian detection")
  Member "autoloss learning discrete schedules for alternate optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "addressing the loss metric mismatch with adaptive loss alignment")
  Member "real to virtual domain unification for end to end autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deceiving image to image translation networks for autonomous driving with adversarial perturbations")
  Member "unsupervised multi target domain adaptation an information theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "entropy minimization vs diversity maximization for domain adaptation")
  Member "domain adaptive faster r cnn for object detection in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "multi target unsupervised domain adaptation without exactly shared categories":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "domain adaptation through transferring both the source knowledge and target relatedness simultaneously")
  Member "deep cocktail network multi source unsupervised domain adaptation with category shift":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "boosting domain adaptation by discovering latent domains":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised domain adaptation via structurally regularized deep clustering")

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)