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: "spottune transfer learning through adaptive fine tuning"
  Abstract: Transfer learning, which allows a source task to affect the inductive bias of the target task, is widely used in computer vision. The typical way of conducting transfer learning with deep neural networks is to fine-tune a model pre-trained on the source task using data from the target task. In this paper, we propose an adaptive fine-tuning approach, called SpotTune, which finds the optimal fine-tuning strategy per instance for the target data. In SpotTune, given an image from the target task, a policy network is used to make routing decisions on whether to pass the image through the fine-tuned layers or the pre-trained layers. We conduct extensive experiments to demonstrate the effectiveness of the proposed approach. Our method outperforms the traditional fine-tuning approach on 12 out of 14 standard datasets.We also compare SpotTune with other state-of-the-art fine-tuning strategies, showing superior performance. On the Visual Decathlon datasets, our method achieves the highest score across the board without bells and whistles.

Connected through hyperedge HE-1 (induced by source paper "agriculture vision a large aerial image database for agricultural pattern analysis"):
  with members:
    - "foal fast online adaptive learning for cardiac motion estimation"
    - "higherhrnet scale aware representation learning for bottom up human pose estimation"
    - "differential treatment for stuff and things a simple unsupervised domain adaptation method for semantic segmentation"
    - "convolutional networks with dense connectivity"
    - "bottom up higher resolution networks for multi person pose estimation"
    - "isaid a large scale dataset for instance segmentation in aerial images"
    - "any precision deep neural networks"
    - "skynet a hardware efficient method for object detection and tracking on embedded systems"

Connected through hyperedge HE-2 (induced by source paper "parameter efficient transfer from sequential behaviors for user profiling and recommendation"):
  with members:
    - "future data helps training modeling future contexts for session based recommendation"
    - "parameter efficient transfer learning for nlp"
    - "albert a lite bert for self supervised learning of language representations"
    - "darec deep domain adaptation for cross domain recommendation via transferring rating patterns"
    - "bert and pals projected attention layers for efficient adaptation in multi task learning"
    - "vl bert pre training of generic visual linguistic representations"
    - "do better imagenet models transfer better"
    - "conet collaborative cross networks for cross domain recommendation"

Connected through hyperedge HE-3 (induced by source paper "layer wise pruning and auto tuning of layer wise learning rates in fine tuning of deep networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "backbone can not be trained at once rolling back to pre trained network for person re identification"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "do better imagenet models transfer better"
    - "classification is a strong baseline for deep metric learning"
    - "learning to navigate for fine grained classification"
    - "deep randomized ensembles for metric learning"

Connected through hyperedge HE-4 (induced by source paper "adaptation of a deep learning malignancy model from full field digital mammography to digital breast tomosynthesis"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning to generalize one sample at a time with self supervision"):
  with members:
    - "episodic training for domain generalization"
    - "adagraph unifying predictive and continuous domain adaptation through graphs"
    - "self supervised generalisation with meta auxiliary learning"
    - "domain generalization by solving jigsaw puzzles"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "grasp2vec learning object representations from self supervised grasping"
    - "boosting self supervised learning via knowledge transfer"
    - "open set domain adaptation by backpropagation"

Connected through hyperedge HE-6 (induced by source paper "adashare learning what to share for efficient deep multi task learning"):
  with members:
    - "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"
    - "deep elastic networks with model selection for multi task learning"
    - "which tasks should be learned together in multi task learning"
    - "branched multi task networks deciding what layers to share"
    - "many task learning with task routing"
    - "evolutionary architecture search for deep multitask networks"
    - "taskonomy disentangling task transfer learning"
    - "exploring shared structures and hierarchies for multiple nlp tasks"

Connected through hyperedge HE-7 (induced by source paper "learning to transfer learn"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "object part attention model for fine grained image classification"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "neural voice cloning with a few samples"
    - "domain adaptive transfer learning with specialist models"
    - "autoaugment learning augmentation policies from data"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "budget aware adapters for multi domain learning"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"
    - "adding new tasks to a single network with weight transformations using binary masks"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "skipnet learning dynamic routing in convolutional networks"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"

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)