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: "hashing in the zero shot framework with domain adaptation"
  Abstract: Techniques to learn hash codes which can store and retrieve large dimensional multimedia data efficiently have attracted broad research interests in the recent years. With rapid explosion of newly emerged concepts and online data, existing supervised hashing algorithms suffer from the problem of scarcity of ground truth annotations due to the high cost of obtaining manual annotations. Therefore, we propose an algorithm to learn a hash function from training images belonging to `seen' classes which can efficiently encode images of `unseen' classes to binary codes. Specifically, we project the image features from visual space and semantic features from semantic space into a common Hamming subspace. Earlier works to generate hash codes have tried to relax the discrete constraints on hash codes and solve the continuous optimization problem. However, it often leads to quantization errors. In this work, we use the max-margin classifier to learn an efficient hash function. To address the concern of domain-shift which may arise due to the introduction of new classes, we also introduce an unsupervised domain adaptation model in the proposed hashing framework. Results on the three datasets show the advantage of using domain adaptation in learning a high-quality hash function and superiority of our method for the task of image retrieval performance as compared to several state-of-the-art hashing methods.

Connected through hyperedge HE-1 (induced by source paper "transductive zero shot hashing for multi label image retrieval"):
  with members:
    - "exploring auxiliary context discrete semantic transfer hashing for scalable image retrieval"
    - "supervised discrete hashing with relaxation"
    - "improved deep hashing with soft pairwise similarity for multi label image retrieval"
    - "deep hashing network for unsupervised domain adaptation"
    - "hashnet deep learning to hash by continuation"
    - "scalable image retrieval by sparse product quantization"
    - "compact hash codes for efficient visual descriptors retrieval in large scale databases"
    - "deep semantic ranking based hashing for multi label image retrieval"

Connected through hyperedge HE-2 (induced by source paper "transductive zero shot hashing via coarse to fine similarity mining"):
  with members:
    - "deep hashing network for unsupervised domain adaptation"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "unsupervised deep hashing for large scale visual search"
    - "fast training of triplet based deep binary embedding networks"
    - "deep cross modal hashing"
    - "transductive multi view zero shot learning"
    - "efficient training of very deep neural networks for supervised hashing"
    - "deep semantic ranking based hashing for multi label image retrieval"

Connected through hyperedge HE-3 (induced by source paper "semantic softmax loss for zero shot learning"):
  with members:
    - "transductive zero shot learning with a self training dictionary approach"
    - "semi supervised vocabulary informed learning"
    - "improving semantic embedding consistency by metric learning for zero shot classification"
    - "transductive multi view zero shot learning"
    - "evaluation of output embeddings for fine grained image classification"
    - "zero shot learning via semantic similarity embedding"
    - "ridge regression hubness and zero shot learning"
    - "predicting deep zero shot convolutional neural networks using textual descriptions"

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)