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: "deep region hashing for efficient large scale instance search from images"
  Abstract: Instance Search (INS) is a fundamental problem for many applications, while it is more challenging comparing to traditional image search since the relevancy is defined at the instance level. #R##N#Existing works have demonstrated the success of many complex ensemble systems that are typically conducted by firstly generating object proposals, and then extracting handcrafted and/or CNN features of each proposal for matching. However, object bounding box proposals and feature extraction are often conducted in two separated steps, thus the effectiveness of these methods collapses. Also, due to the large amount of generated proposals, matching speed becomes the bottleneck that limits its application to large-scale datasets. To tackle these issues, in this paper we propose an effective and efficient Deep Region Hashing (DRH) approach for large-scale INS using an image patch as the query. Specifically, DRH is an end-to-end deep neural network which consists of object proposal, feature extraction, and hash code generation. DRH shares full-image convolutional feature map with the region proposal network, thus enabling nearly cost-free region proposals. Also, each high-dimensional, real-valued region features are mapped onto a low-dimensional, compact binary codes for the efficient object region level matching on large-scale dataset. Experimental results on four datasets show that our DRH can achieve even better performance than the state-of-the-arts in terms of MAP, while the efficiency is improved by nearly 100 times.

Connected through hyperedge HE-1 (induced by source paper "deep ordinal hashing with spatial attention"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "end to end comparative attention networks for person re identification"
    - "supervising neural attention models for video captioning by human gaze data"
    - "fooling vision and language models despite localization and attention mechanism"
    - "image captioning with semantic attention"
    - "weakly supervised cascaded convolutional networks"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-2 (induced by source paper "self supervised video hashing with hierarchical binary auto encoder"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "adversarial learning for neural dialogue generation"
    - "hashnet deep learning to hash by continuation"
    - "massive exploration of neural machine translation architectures"
    - "image captioning with semantic attention"
    - "a survey on learning to hash"
    - "yfcc100m the new data in multimedia research"

Connected through hyperedge HE-3 (induced by source paper "unsupervised deep object discovery for instance recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "fast spectral ranking for similarity search"
    - "context aware query image representation for particular object retrieval"
    - "faster r cnn features for instance search"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "large scale image retrieval with attentive deep local features"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"

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)