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: "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
  Abstract: Fine-grained visual categorization (FGVC) is challenging due in part to the fact that it is often difficult to acquire an enough number of training samples. To employ large models for FGVC without suffering from overfitting, existing methods usually adopt a strategy of pre-training the models using a rich set of auxiliary data, followed by fine-tuning on the target FGVC task. However, the objective of pre-training does not take the target task into account, and consequently such obtained models are suboptimal for fine-tuning. To address this issue, we propose in this paper a new deep FGVC model termed MetaFGNet. Training of MetaFGNet is based on a novel regularized meta-learning objective, which aims to guide the learning of network parameters so that they are optimal for adapting to the target FGVC task. Based on MetaFGNet, we also propose a simple yet effective scheme for selecting more useful samples from the auxiliary data. Experiments on benchmark FGVC datasets show the efficacy of our proposed method.

Connected through hyperedge HE-1 (induced by source paper "unsupervised domain adaptation via structurally regularized deep clustering"):
  with members:
    - "unsupervised multi class domain adaptation theory algorithms and practice"
    - "discriminative adversarial domain adaptation"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "orthogonal deep neural networks"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "domain specific batch normalization for unsupervised domain adaptation"
    - "domain symmetric networks for adversarial domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "locality and compositionality in zero shot learning"):
  with members:
    - "visual space optimization for zero shot learning"
    - "measuring compositionality in representation learning"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "learning representations by maximizing mutual information across views"
    - "are few shot learning benchmarks too simple"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "bilinear attention networks"

Connected through hyperedge HE-3 (induced by source paper "discriminative adversarial domain adaptation"):
  with members:
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "domain symmetric networks for adversarial domain adaptation"
    - "open set domain adaptation for image and action recognition"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "unsupervised visual domain adaptation a deep max margin gaussian process approach"
    - "looking back at labels a class based domain adaptation technique"
    - "gotta adapt em all joint pixel and feature level domain adaptation for recognition in the wild"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "semantic segmentation from remote sensor data and the exploitation of latent learning for classification of auxiliary tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self supervised generalisation with meta auxiliary learning"
    - "temporary removal aerial imagery for roof segmentation a large scale dataset towards automatic mapping of buildings"
    - "automatic pixelwise object labeling for aerial imagery using stacked u nets"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "auxiliary tasks in multi task learning"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "self supervised generalisation with meta auxiliary learning"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "cinic 10 is not imagenet or cifar 10"
    - "auxiliary tasks in multi task learning"
    - "end to end multi task learning with attention"
    - "focal loss for dense object detection"
    - "prototypical networks for few shot learning"
    - "unsupervised learning of depth and ego motion from video"
    - "multi task self supervised visual learning"

Connected through hyperedge HE-6 (induced by source paper "deep learning for fine grained image analysis a survey"):
  with members:
    - "piecewise classifier mappings learning fine grained learners for novel categories with few examples"
    - "rpc a large scale retail product checkout dataset"
    - "multi attention multi class constraint for fine grained image recognition"
    - "maximum entropy fine grained classification"
    - "learning to navigate for fine grained classification"
    - "coarse to fine a rnn based hierarchical attention model for vehicle re identification"
    - "part aligned bilinear representations for person re identification"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "parn position aware relation networks for few shot learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "memory matching networks for one shot image recognition"
    - "non local neural networks"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"
    - "prototypical networks for few shot learning"
    - "deformable convolutional networks"
    - "understanding the effective receptive field in deep convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "a simple and effective postprocessing method for image classification"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "dropblock a regularization method for convolutional networks"
    - "deep learning for image denoising a survey"
    - "normface l 2 hypersphere embedding for face verification"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "a latent variable model approach to pmi based word embeddings"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)