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: "one shot adaptation of supervised deep convolutional models"
  Abstract: Dataset bias remains a significant barrier towards solving real world computer vision tasks. Though deep convolutional networks have proven to be a competitive approach for image classification, a question remains: have these models have solved the dataset bias problem? In general, training or fine-tuning a state-of-the-art deep model on a new domain requires a significant amount of data, which for many applications is simply not available. Transfer of models directly to new domains without adaptation has historically led to poor recognition performance. In this paper, we pose the following question: is a single image dataset, much larger than previously explored for adaptation, comprehensive enough to learn general deep models that may be effectively applied to new image domains? In other words, are deep CNNs trained on large amounts of labeled data as susceptible to dataset bias as previous methods have been shown to be? We show that a generic supervised deep CNN model trained on a large dataset reduces, but does not remove, dataset bias. Furthermore, we propose several methods for adaptation with deep models that are able to operate with little (one example per category) or no labeled domain specific data. Our experiments show that adaptation of deep models on benchmark visual domain adaptation datasets can provide a significant performance boost.

Connected through hyperedge HE-1 (induced by source paper "are we done with object recognition the icub robot s perspective"):
  with members:
    - "robotic tactile perception of object properties a review"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deep image retrieval learning global representations for image search"
    - "improved deep learning of object category using pose information"
    - "the curious robot learning visual representations via physical interactions"
    - "physically based rendering for indoor scene understanding using convolutional neural networks"
    - "a deep representation for depth images from synthetic data"
    - "learning to recognize objects by retaining other factors of variation"

Connected through hyperedge HE-2 (induced by source paper "diversity in faces"):
  with members:
    - "the devil of face recognition is in the noise"
    - "women also snowboard overcoming bias in captioning models"
    - "every smile is unique landmark guided diverse smile generation"
    - "arcface additive angular margin loss for deep face recognition"
    - "segmentation of both diseased and healthy skin from clinical photographs in a primary care setting"
    - "bias mitigation post processing for individual and group fairness"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-3 (induced by source paper "when unsupervised domain adaptation meets tensor representations"):
  with members:
    - "return of frustratingly easy domain adaptation"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-4 (induced by source paper "fast rates by transferring from auxiliary hypotheses"):
  with members:
    - "transfer learning through greedy subset selection"
    - "optimistic rates for learning with a smooth loss"
    - "regularization techniques for learning with matrices"
    - "frustratingly easy domain adaptation"
    - "domain adaptation learning bounds and algorithms"
    - "learning to learn"

Connected through hyperedge HE-5 (induced by source paper "hierarchical multiscale recurrent neural networks"):
  with members:
    - "surprisal driven feedback in recurrent networks"
    - "one shot learning with memory augmented neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "deep networks with stochastic depth"
    - "recurrent memory array structures"
    - "recurrent highway networks"
    - "strategic attentive writer for learning macro actions"
    - "theano a python framework for fast computation of mathematical expressions"

Connected through hyperedge HE-6 (induced by source paper "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"):
  with members:
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "character level chinese writer identification using path signature feature dropstroke and deep cnn"
    - "learning transferable features with deep adaptation networks"
    - "reading scene text in deep convolutional sequences"
    - "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "high performance offline handwritten chinese character recognition using googlenet and directional feature maps"

Connected through hyperedge HE-7 (induced by source paper "multi task curriculum transfer deep learning of clothing attributes"):
  with members:
    - "transductive multi view zero shot learning"
    - "simultaneous deep transfer across domains and tasks"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "deep human parsing with active template regression"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "deep reconstruction classification networks for unsupervised domain adaptation"):
  with members:
    - "simultaneous deep transfer across domains and tasks"
    - "learning transferable features with deep adaptation networks"
    - "scatter component analysis a unified framework for domain adaptation and domain generalization"
    - "very deep convolutional networks for large scale image recognition"
    - "deep domain confusion maximizing for domain invariance"
    - "domain adaptive neural networks for object recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "generalized denoising auto encoders as generative models"

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)