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: "revisiting batch normalization for practical domain adaptation"
  Abstract: Deep neural networks (DNN) have shown unprecedented success in various computer vision applications such as image classification and object detection. However, it is still a common annoyance during the training phase, that one has to prepare at least thousands of labeled images to fine-tune a network to a specific domain. Recent study (Tommasi et al. 2015) shows that a DNN has strong dependency towards the training dataset, and the learned features cannot be easily transferred to a different but relevant task without fine-tuning. In this paper, we propose a simple yet powerful remedy, called Adaptive Batch Normalization (AdaBN) to increase the generalization ability of a DNN. By modulating the statistics in all Batch Normalization layers across the network, our approach achieves deep adaptation effect for domain adaptation tasks. In contrary to other deep learning domain adaptation methods, our method does not require additional components, and is parameter-free. It archives state-of-the-art performance despite its surprising simplicity. Furthermore, we demonstrate that our method is complementary with other existing methods. Combining AdaBN with existing domain adaptation treatments may further improve model performance.

Connected through hyperedge HE-1 (induced by source paper "cross domain few shot classification via learned feature wise transformation"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "feature critic networks for heterogeneous domain generalization"
    - "dense classification and implanting for few shot learning"
    - "meta learning with differentiable convex optimization"
    - "progressive domain adaptation for object detection"
    - "a closer look at few shot classification"
    - "semantic image synthesis with spatially adaptive normalization"
    - "guided image to image translation with bi directional feature transformation"

Connected through hyperedge HE-2 (induced by source paper "knowledge distillation via adaptive instance normalization"):
  with members:
    - "on the efficacy of knowledge distillation"
    - "relational knowledge distillation"
    - "adaptis adaptive instance selection network"
    - "few shot adversarial learning of realistic neural talking head models"
    - "xnor net improved binary neural networks"
    - "a comprehensive overhaul of feature distillation"
    - "contrastive representation distillation"
    - "similarity preserving knowledge distillation"

Connected through hyperedge HE-3 (induced by source paper "disassembling the dataset a camera alignment mechanism for multiple tasks in person re identification"):
  with members:
    - "view confusion feature learning for person re identification"
    - "resolution invariant person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "eanet enhancing alignment for cross domain person re identification"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "maskreid a mask based deep ranking neural network for person re identification"

Connected through hyperedge HE-4 (induced by source paper "class conditional alignment for partial domain adaptation"):
  with members:
    - "learning to transfer examples for partial domain adaptation"
    - "deep visual domain adaptation a survey"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "multi adversarial domain adaptation"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "autodial automatic domain alignment layers"
    - "deep hashing network for unsupervised domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "universal domain adaptation through self supervision"):
  with members:
    - "unsupervised deep learning by neighbourhood discovery"
    - "momentum contrast for unsupervised visual representation learning"
    - "domain specific batch normalization for unsupervised domain adaptation"
    - "domain generalization by solving jigsaw puzzles"
    - "bridging theory and algorithm for domain adaptation"
    - "learning to transfer examples for partial domain adaptation"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "semi supervised domain adaptation via minimax entropy"

Connected through hyperedge HE-6 (induced by source paper "m adda unsupervised domain adaptation with deep metric learning"):
  with members:
    - "deep visual domain adaptation a survey"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "unsupervised domain adaptation with similarity learning"
    - "adversarial discriminative domain adaptation"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "self ensembling for visual domain adaptation"
    - "transfer learning by ranking for weakly supervised object annotation"
    - "from source to target and back symmetric bi directional adaptive gan"

Connected through hyperedge HE-7 (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-8 (induced by source paper "self supervised domain adaptation for computer vision tasks"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "domain generalization by solving jigsaw puzzles"
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "revisiting self supervised visual representation learning"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"

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)