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: "instance aware hashing for multi label image retrieval"
  Abstract: Similarity-preserving hashing is a commonly used method for nearest neighbor search in large-scale image retrieval. For image retrieval, deep-network-based hashing methods are appealing, since they can simultaneously learn effective image representations and compact hash codes. This paper focuses on deep-network-based hashing for multi-label images, each of which may contain objects of multiple categories. In most existing hashing methods, each image is represented by one piece of hash code, which is referred to as semantic hashing. This setting may be suboptimal for multi-label image retrieval. To solve this problem, we propose a deep architecture that learns instance-aware image representations for multi-label image data, which are organized in multiple groups, with each group containing the features for one category. The instance-aware representations not only bring advantages to semantic hashing but also can be used in category-aware hashing, in which an image is represented by multiple pieces of hash codes and each piece of code corresponds to a category. Extensive evaluations conducted on several benchmark data sets demonstrate that for both the semantic hashing and the category-aware hashing, the proposed method shows substantial improvement over the state-of-the-art supervised and unsupervised hashing methods.

Connected through hyperedge HE-1 (induced by source paper "simultaneous region localization and hash coding for fine grained image retrieval"):
  with members:
    - "fast supervised discrete hashing"
    - "deep policy hashing network with listwise supervision"
    - "deep triplet quantization"
    - "feature pyramid hashing"
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "multi attention multi class constraint for fine grained image recognition"
    - "deep semantic hashing with generative adversarial networks"
    - "learning to navigate for fine grained classification"

Connected through hyperedge HE-2 (induced by source paper "semantic cluster unary loss for efficient deep hashing"):
  with members:
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "deep reinforcement learning for image hashing"
    - "mihash online hashing with mutual information"
    - "deep supervised discrete hashing"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "no fuss distance metric learning using proxies"

Connected through hyperedge HE-3 (induced by source paper "texture synthesis guided deep hashing for texture image retrieval"):
  with members:
    - "local neighborhood intensity pattern a new texture feature descriptor for image retrieval"
    - "non stationary texture synthesis by adversarial expansion"
    - "efficient nearest neighbors search for large scale landmark recognition"
    - "hashing with binary matrix pursuit"
    - "deep supervised discrete hashing"
    - "texturegan controlling deep image synthesis with texture patches"
    - "fine tuning cnn image retrieval with no human annotation"
    - "hashnet deep learning to hash by continuation"

Connected through hyperedge HE-4 (induced by source paper "directional statistics based deep metric learning for image classification and retrieval"):
  with members:
    - "triplet center loss for multi view 3d object retrieval"
    - "sphereface deep hypersphere embedding for face recognition"
    - "prototypical networks for few shot learning"
    - "von mises fisher mixture model based deep learning application to face verification"
    - "part based deep hashing for large scale person re identification"
    - "rethinking feature discrimination and polymerization for large scale recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "fast training of triplet based deep binary embedding networks"

Connected through hyperedge HE-5 (induced by source paper "improved deep hashing with soft pairwise similarity for multi label image retrieval"):
  with members:
    - "supervised discrete hashing with relaxation"
    - "deep supervised discrete hashing"
    - "hashnet deep learning to hash by continuation"
    - "deep supervised hashing with triplet labels"
    - "scalable image retrieval by sparse product quantization"
    - "deep cross modal hashing"
    - "compact hash codes for efficient visual descriptors retrieval in large scale databases"
    - "robust gait recognition by integrating inertial and rgbd sensors"

Connected through hyperedge HE-6 (induced by source paper "part based deep hashing for large scale person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "semisupervised feature analysis by mining correlations among multiple tasks"
    - "end to end comparative attention networks for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "svdnet for pedestrian retrieval"
    - "temporal model adaptation for person re identification"
    - "deep attributes driven multi camera person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"

Connected through hyperedge HE-7 (induced by source paper "deep label distribution learning with label ambiguity"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "exploit bounding box annotations for multi label object recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "robust optimization for deep regression"
    - "label distribution learning"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "selective convolutional descriptor aggregation for fine grained image retrieval"):
  with members:
    - "good practice in cnn feature transfer"
    - "weakly supervised fine grained categorization with part based image representation"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"
    - "deepproposal hunting objects by cascading deep convolutional layers"

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)