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: "joint intermodal and intramodal label transfers for extremely rare or unseen classes"
  Abstract: In this paper, we present a label transfer model from texts to images for image classification tasks. The problem of image classification is often much more challenging than text classification. On one hand, labeled text data is more widely available than the labeled images for classification tasks. On the other hand, text data tends to have natural semantic interpretability, and they are often more directly related to class labels. On the contrary, the image features are not directly related to concepts inherent in class labels. One of our goals in this paper is to develop a model for revealing the functional relationships between text and image features as to directly transfer intermodal and intramodal labels to annotate the images. This is implemented by learning a transfer function as a bridge to propagate the labels between two multimodal spaces. However, the intermodal label transfers could be undermined by blindly transferring the labels of noisy texts to annotate images. To mitigate this problem, we present an intramodal label transfer process, which complements the intermodal label transfer by transferring the image labels instead when relevant text is absent from the source corpus. In addition, we generalize the inter-modal label transfer to zero-shot learning scenario where there are only text examples available to label unseen classes of images without any positive image examples. We evaluate our algorithm on an image classification task and show the effectiveness with respect to the other compared algorithms.

Connected through hyperedge HE-1 (induced by source paper "adaptive locality preserving regression"):
  with members:
    - "deep linear discriminant analysis"
    - "deep residual learning for image recognition"
    - "collaborative representation based classification for face recognition"

Connected through hyperedge HE-2 (induced by source paper "category anchor guided unsupervised domain adaptation for semantic segmentation"):
  with members:
    - "famed net a fast and accurate multi scale end to end dehazing network"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "fully point wise convolutional neural network for modeling statistical regularities in natural images"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "cross domain weakly supervised object detection through progressive domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "heterogeneous graph based knowledge transfer for generalized zero shot learning"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "generalized zero shot recognition based on visually semantic embedding"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "prototypical networks for few shot learning"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"

Connected through hyperedge HE-4 (induced by source paper "structure fusion and propagation for zero shot learning"):
  with members:
    - "discriminative learning of latent features for zero shot recognition"
    - "visual data synthesis via gan for zero shot video classification"
    - "transductive zero shot learning with a self training dictionary approach"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "zero shot learning the good the bad and the ugly"
    - "label embedding for image classification"
    - "synthesized classifiers for zero shot learning"
    - "latent embeddings for zero shot classification"

Connected through hyperedge HE-5 (induced by source paper "complementary attributes a new clue to zero shot learning"):
  with members:
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "dynamic routing between capsules"
    - "learning to compare relation network for few shot learning"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "learning a deep embedding model for zero shot learning"
    - "label embedding for image classification"
    - "synthesized classifiers for zero shot learning"

Connected through hyperedge HE-6 (induced by source paper "tagging like humans diverse and distinct image annotation"):
  with members:
    - "multi label learning with missing labels using mixed dependency graphs"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"
    - "improving pairwise ranking for multi label image classification"
    - "annotation order matters recurrent image annotator for arbitrary length image tagging"
    - "semantic regularisation for recurrent image annotation"
    - "cnn rnn a unified framework for multi label image classification"

Connected through hyperedge HE-7 (induced by source paper "personalized age progression with bi level aging dictionary learning"):
  with members:
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "personalized age progression with aging dictionary"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-8 (induced by source paper "recent advances in zero shot recognition"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "progressive neural networks"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "zero shot learning the good the bad and the ugly"
    - "dynamic concept composition for zero example event detection"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "matching networks for one shot learning"

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)