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: "bayes merging of multiple vocabularies for scalable image retrieval"
  Abstract: The Bag-of-Words (BoW) representation is well applied to recent state-of-the-art image retrieval works. Typically, multiple vocabularies are generated to correct quantization artifacts and improve recall. However, this routine is corrupted by vocabulary correlation, i.e., overlapping among different vocabularies. Vocabulary correlation leads to an over-counting of the indexed features in the overlapped area, or the intersection set, thus compromising the retrieval accuracy. In order to address the correlation problem while preserve the benefit of high recall, this paper proposes a Bayes merging approach to down-weight the indexed features in the intersection set. Through explicitly modeling the correlation problem in a probabilistic view, a joint similarity on both image- and feature-level is estimated for the indexed features in the intersection set. #R##N#We evaluate our method through extensive experiments on three benchmark datasets. Albeit simple, Bayes merging can be well applied in various merging tasks, and consistently improves the baselines on multi-vocabulary merging. Moreover, Bayes merging is efficient in terms of both time and memory cost, and yields competitive performance compared with the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "attribute guided sketch generation"):
  with members:
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "instagan instance aware image to image translation"
    - "r btn cross domain face composite and synthesis from limited facial patches"
    - "deformable gans for pose based human image generation"
    - "image generation from sketch constraint using contextual gan"
    - "unsupervised creation of parameterized avatars"
    - "unsupervised diverse colorization via generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "gesturegan for hand gesture to gesture translation in the wild"):
  with members:
    - "every smile is unique landmark guided diverse smile generation"
    - "face aging with contextual generative adversarial nets"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "geometry contrastive gan for facial expression transfer"
    - "cross domain human parsing via adversarial feature and label adaptation"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "dual discriminator generative adversarial nets"
    - "transformation grounded image generation network for novel 3d view synthesis"

Connected through hyperedge HE-3 (induced by source paper "vehicle re identification using quadruple directional deep learning features"):
  with members:
    - "a discriminatively learned cnn embedding for person reidentification"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "deep hybrid similarity learning for person re identification"
    - "learning a discriminative null space for person re identification"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "a large scale car dataset for fine grained categorization and verification"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "recent advance in content based image retrieval a literature survey"):
  with members:
    - "deep image retrieval learning global representations for image search"
    - "sketch based 3d shape retrieval using convolutional neural networks"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "visual instance retrieval with deep convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "sift meets cnn a decade survey of instance retrieval"):
  with members:
    - "polysemous codes"
    - "faster r cnn features for instance search"
    - "lift learned invariant feature transform"
    - "end to end learning of deep visual representations for image retrieval"
    - "bags of local convolutional features for scalable instance search"
    - "deep image retrieval learning global representations for image search"
    - "good practice in cnn feature transfer"
    - "a survey on learning to hash"

Connected through hyperedge HE-6 (induced by source paper "coarse2fine two layer fusion for image retrieval"):
  with members:
    - "visual reranking with improved image graph"
    - "packing and padding coupled multi index for accurate image retrieval"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "image retrieval with hierarchical matching pursuit"):
  with members:
    - "seeing the big picture deep embedding with contextual evidences"
    - "visual reranking with improved image graph"
    - "packing and padding coupled multi index for accurate image retrieval"

Connected through hyperedge HE-8 (induced by source paper "visual reranking with improved image graph"):
  with members:
    - "packing and padding coupled multi index for accurate image retrieval"

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)