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: "lightweight unsupervised domain adaptation by convolutional filter reconstruction"
  Abstract: End-to-end learning methods have achieved impressive results in many areas of computer vision. At the same time, these methods still suffer from a degradation in performance when testing on new datasets that stem from a different distribution. This is known as the domain shift effect. Recently proposed adaptation methods focus on retraining the network parameters. However, this requires access to all (labeled) source data, a large amount of (unlabeled) target data, and plenty of computational resources. In this work, we propose a lightweight alternative, that allows adapting to the target domain based on a limited number of target samples in a matter of minutes rather than hours, days or even weeks. To this end, we first analyze the output of each convolutional layer from a domain adaptation perspective. Surprisingly, we find that already at the very first layer, domain shift effects pop up. We then propose a new domain adaptation method, where first layer convolutional filters that are badly affected by the domain shift are reconstructed based on less affected ones. This improves the performance of the deep network on various benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "learning to see across domains and modalities"):
  with members:
    - "boosting domain adaptation by discovering latent domains"
    - "generalizing across domains via cross gradient training"
    - "learning from noisy web data with category level supervision"
    - "kitting in the wild through online domain adaptation"
    - "robust place categorization with deep domain generalization"
    - "deep hashing network for unsupervised domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "deep layer aggregation"

Connected through hyperedge HE-2 (induced by source paper "continual learning in neural networks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "knowledge transfer in deep block modular neural networks"
    - "meta learning representations for continual learning"
    - "continual reinforcement learning in 3d non stationary environments"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"

Connected through hyperedge HE-3 (induced by source paper "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"):
  with members:
    - "image to image translation for domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "adversarial discriminative domain adaptation"
    - "self ensembling for visual domain adaptation"
    - "unsupervised image to image translation networks"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "generate to adapt aligning domains using generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "boosting domain adaptation by discovering latent domains"):
  with members:
    - "deeper broader and artier domain generalization"
    - "just dial domain alignment layers for unsupervised domain adaptation"
    - "deep reconstruction classification networks for unsupervised domain adaptation"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "learning from simulated and unsupervised images through adversarial training"
    - "revisiting batch normalization for practical domain adaptation"
    - "deep coral correlation alignment for deep domain adaptation"
    - "domain separation networks"

Connected through hyperedge HE-5 (induced by source paper "domain adaptation for visual applications a comprehensive survey"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "generalized multi view embedding for visual recognition and cross modal retrieval"
    - "adversarial discriminative domain adaptation"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "play and learn using video games to train computer vision models"
    - "deep transfer learning with joint adaptation networks"
    - "coupled generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "zero shot deep domain adaptation"):
  with members:
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "a compact dnn approaching googlenet level accuracy of classification and domain adaptation"
    - "deep multimodal representation learning from temporal data"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "addressing appearance change in outdoor robotics with adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "autodial automatic domain alignment layers"):
  with members:
    - "asymmetric tri training for unsupervised domain adaptation"
    - "just dial domain alignment layers for unsupervised domain adaptation"
    - "revisiting batch normalization for practical domain adaptation"
    - "deep transfer learning with joint adaptation networks"
    - "deep coral correlation alignment for deep domain adaptation"
    - "domain separation networks"
    - "unsupervised domain adaptation with residual transfer networks"
    - "learning the roots of visual domain shift"

Connected through hyperedge HE-8 (induced by source paper "fine grained recognition in the wild a multi task domain adaptation approach"):
  with members:
    - "pixel level domain transfer"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "domain separation networks"
    - "unsupervised domain adaptation with residual transfer networks"
    - "a large scale car dataset for fine grained categorization and verification"
    - "instance aware semantic segmentation via multi task network cascades"
    - "simultaneous deep transfer across domains and tasks"

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.CV(Computer Vision and Pattern Recognition)