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: "explicit inductive bias for transfer learning with convolutional networks"
  Abstract: In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch. When using fine-tuning, the underlying assumption is that the pre-trained model extracts generic features, which are at least partially relevant for solving the target task, but would be difficult to extract from the limited amount of data available on the target task. However, besides the initialization with the pre-trained model and the early stopping, there is no mechanism in fine-tuning for retaining the features learned on the source task. In this paper, we investigate several regularization schemes that explicitly promote the similarity of the final solution with the initial model. We eventually recommend a simple $L^2$ penalty using the pre-trained model as a reference, and we show that this approach behaves much better than the standard scheme using weight decay on a partially frozen network.

Connected through hyperedge HE-1 (induced by source paper "freeze discriminator a simple baseline for fine tuning gans"):
  with members:
    - "learning what and where to transfer"
    - "consistency regularization for generative adversarial networks"
    - "analyzing and improving the image quality of stylegan"
    - "relational knowledge distillation"
    - "momentum contrast for unsupervised visual representation learning"
    - "few shot adversarial learning of realistic neural talking head models"
    - "stargan v2 diverse image synthesis for multiple domains"
    - "few shot unsupervised image to image translation"

Connected through hyperedge HE-2 (induced by source paper "learning to continually learn"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "meta learning representations for continual learning"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "continuous learning in single incremental task scenarios"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"

Connected through hyperedge HE-3 (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-4 (induced by source paper "weakly supervised complementary parts models for fine grained image classification from the bottom up"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "multi attention multi class constraint for fine grained image recognition"
    - "features for multi target multi camera tracking and re identification"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "network dissection quantifying interpretability of deep visual representations"
    - "blitznet a real time deep network for scene understanding"

Connected through hyperedge HE-5 (induced by source paper "pseudo task regularization for convnet transfer learning"):
  with members:
    - "pairwise confusion for fine grained visual classification"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "disturblabel regularizing cnn on the loss layer"
    - "understanding deep learning requires rethinking generalization"
    - "learning without forgetting"
    - "low shot visual recognition by shrinking and hallucinating features"

Connected through hyperedge HE-6 (induced by source paper "adafilter adaptive filter fine tuning for deep transfer learning"):
  with members:
    - "depthwise convolution is all you need for learning multiple visual domains"
    - "transferable end to end aspect based sentiment analysis with selective adversarial learning"
    - "continual lifelong learning with neural networks a review"
    - "do better imagenet models transfer better"
    - "a survey on methods and theories of quantized neural networks"
    - "spottune transfer learning through adaptive fine tuning"
    - "co regularized alignment for unsupervised domain adaptation"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-7 (induced by source paper "towards understanding the transferability of deep representations"):
  with members:
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "averaging weights leads to wider optima and better generalization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "lautum regularization for semi supervised transfer learning"):
  with members:
    - "entropy and mutual information in models of deep neural networks"
    - "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"
    - "taskonomy disentangling task transfer learning"
    - "a survey on deep transfer learning"
    - "semi supervised learning with declaratively specified entropy constraints"
    - "generalization error in deep learning"
    - "when semi supervised learning meets transfer learning training strategies models and datasets"
    - "realistic evaluation of deep semi supervised learning algorithms"

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)