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: "growing a brain fine tuning by increasing model capacity"
  Abstract: CNNs have made an undeniable impact on computer vision through the ability to learn high-capacity models with large annotated training sets. One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset. This is usually accomplished through fine-tuning a fixed-size network on new target data. Indeed, virtually every contemporary visual recognition system makes use of fine-tuning to transfer knowledge from ImageNet. In this work, we analyze what components and parameters change during fine-tuning, and discover that increasing model capacity allows for more natural model adaptation through fine-tuning. By making an analogy to developmental learning, we demonstrate that "growing" a CNN with additional units, either by widening existing layers or deepening the overall network, significantly outperforms classic fine-tuning approaches. But in order to properly grow a network, we show that newly-added units must be appropriately normalized to allow for a pace of learning that is consistent with existing units. We empirically validate our approach on several benchmark datasets, producing state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "scaling laws for neural language models"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "autogrow automatic layer growing in deep convolutional networks"
    - "a constructive prediction of the generalization error across scales"
    - "universal transformers"
    - "generating long sequences with sparse transformers"
    - "one epoch is all you need"

Connected through hyperedge HE-2 (induced by source paper "scail classifier weights scaling for class incremental learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "incremental learning with maximum entropy regularization rethinking forgetting and intransigence"
    - "large scale incremental learning"
    - "m2kd multi model and multi level knowledge distillation for incremental learning"
    - "do better imagenet models transfer better"
    - "revisiting distillation and incremental classifier learning"
    - "deesil deep shallow incremental learning"
    - "end to end incremental learning"

Connected through hyperedge HE-3 (induced by source paper "improving the adversarial robustness of transfer learning via noisy feature distillation"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning"
    - "legr filter pruning via learned global ranking"
    - "transfusion understanding transfer learning for medical imaging"
    - "adversarially robust transfer learning"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "mixout effective regularization to finetune large scale pretrained language models"
    - "rethinking imagenet pre training"

Connected through hyperedge HE-4 (induced by source paper "budgeted training rethinking deep neural network training under resource constraints"):
  with members:
    - "learnable embedding space for efficient neural architecture compression"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "deep rigid instance scene flow"
    - "gradient descent finds global minima of deep neural networks"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "darts differentiable architecture search"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-5 (induced by source paper "online meta learning"):
  with members:
    - "learning to learn stochastic gradient descent with biased regularization"
    - "introduction to online convex optimization"
    - "provable guarantees for gradient based meta learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "truncated back propagation for bilevel optimization"
    - "how to train your maml"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "deep online learning via meta learning continual adaptation for model based rl"

Connected through hyperedge HE-6 (induced by source paper "self supervised learning of class embeddings from video"):
  with members:
    - "learning landmarks from unaligned data using image translation"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "self supervised learning of a facial attribute embedding from video"
    - "self supervised spatiotemporal feature learning via video rotation prediction"
    - "unsupervised discovery of object landmarks as structural representations"
    - "a fully progressive approach to single image super resolution"
    - "voxceleb2 deep speaker recognition"
    - "self supervised video representation learning with space time cubic puzzles"

Connected through hyperedge HE-7 (induced by source paper "multifaceted analysis of fine tuning in deep model for visual recognition"):
  with members:
    - "gabor convolutional networks"
    - "taskonomy disentangling task transfer learning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "learning transferable features with deep adaptation networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "deep virtual networks for memory efficient inference of multiple tasks"):
  with members:
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "convolutional networks with adaptive inference graphs"
    - "continual learning through synaptic intelligence"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "nestednet learning nested sparse structures in deep 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)