human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "deep transfer learning with joint adaptation networks"
  Abstract: Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain. In this paper, we present joint adaptation networks (JAN), which learn a transfer network by aligning the joint distributions of multiple domain-specific layers across domains based on a joint maximum mean discrepancy (JMMD) criterion. Adversarial training strategy is adopted to maximize JMMD such that the distributions of the source and target domains are made more distinguishable. Learning can be performed by stochastic gradient descent with the gradients computed by back-propagation in linear-time. Experiments testify that our model yields state of the art results on standard datasets.

Connected through hyperedge HE-1 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "orthogonal deep neural networks"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "beyond sharing weights for deep domain adaptation"
    - "domain symmetric networks for adversarial domain adaptation"
    - "bridging theory and algorithm for domain adaptation"
    - "learning to transfer examples for partial domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "multi source distilling domain adaptation"
    - "moment matching for multi source domain adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "algorithms and theory for multiple source adaptation"

Connected through hyperedge HE-3 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation"):
  with members:
    - "drop to adapt learning discriminative features for unsupervised domain adaptation"
    - "domain agnostic learning with disentangled representations"
    - "towards federated learning at scale system design"
    - "when does label smoothing help"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "pseudo labeling curriculum for unsupervised domain adaptation"
    - "learning to transfer examples for partial domain adaptation"
    - "cluster alignment with a teacher for unsupervised domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "harmonizing transferability and discriminability for adapting object detectors"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "self training and adversarial background regularization for unsupervised domain adaptive one stage object detection"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "multi adversarial faster rcnn for unrestricted object detection"
    - "exploring object relation in mean teacher for cross domain detection"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "cluster alignment with a teacher for unsupervised domain adaptation"
    - "constructing self motivated pyramid curriculums for cross domain semantic segmentation a non adversarial approach"

Connected through hyperedge HE-5 (induced by source paper "unbiased mean teacher for cross domain object detection"):
  with members:
    - "self training and adversarial background regularization for unsupervised domain adaptive one stage object detection"
    - "scl towards accurate domain adaptive object detection via gradient detach based stacked complementary losses"
    - "multi adversarial faster rcnn for unrestricted object detection"
    - "exploring object relation in mean teacher for cross domain detection"
    - "a robust learning approach to domain adaptive object detection"
    - "automatic adaptation of object detectors to new domains using self training"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "wasserstein distance based domain adaptation for object detection"

Connected through hyperedge HE-6 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "autosem automatic task selection and mixing in multi task learning"
    - "a survey of domain adaptation for neural machine translation"
    - "deep visual domain adaptation a survey"
    - "multi source domain adaptation with mixture of experts"
    - "unsupervised domain adaptation based on source guided discrepancy"
    - "deep contextualized word representations"
    - "multi source cross lingual model transfer learning what to share"

Connected through hyperedge HE-7 (induced by source paper "domain adaptive ensemble learning"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "episodic training for domain generalization"
    - "consistency regularization for generative adversarial networks"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "randaugment practical data augmentation with no separate search"
    - "domain generalization by solving jigsaw puzzles"
    - "semi supervised domain adaptation via minimax entropy"

Connected through hyperedge HE-8 (induced by source paper "unsupervised domain adaptive object detection using forward backward cyclic adaptation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "beyond sharing weights for deep domain adaptation"
    - "on first order meta learning algorithms"
    - "semantic foggy scene understanding with synthetic data"
    - "reptile a scalable metalearning algorithm"
    - "maximum entropy fine grained classification"
    - "domain adaptive faster r cnn for object detection in the wild"

Question: Please tell me which class the center node 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)