human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "binary constrained deep hashing network for image retrieval without human intervention"):
  Abstract: Learning compact binary codes for image retrieval problem using deep neural networks has attracted increasing attention recently. However, training deep hashing networks is challenging due to the binary constraints on the hash codes, the similarity preserving properties, and the requirement for a vast amount of labelled images. To the best of our knowledge, none of the existing methods has tackled all of these challenges completely in a unified framework. In this work, we propose a novel end-toend deep hashing approach, which is trained to produce binary codes directly from image pixels without human intervention. In particular, our main contribution is to propose a novel pairwise loss function, which simultaneously encodes the distances between pairs of binary codes, and the binary quantization error. We propose an efficient parameter learning algorithm for this loss function. In addition, to provide similar/dissimilar images for our pairwise loss function, we exploit 3D models reconstructed from unlabeled images for automatic generation of enormous similar/dissimilar pairs. Extensive experiments on three image retrieval benchmark datasets demonstrate the superior performance of the proposed method.

Includes members:
  Member "supervised learning of semantics preserving hash via deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "random vlad based deep hashing for efficient image retrieval")
  Member "simultaneous feature aggregating and hashing for large scale image search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "simultaneous feature aggregating and hashing for compact binary code learning")
  Member "selective deep convolutional features for image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combination of multiple global descriptors for image retrieval")
  Member "affordancenet an end to end deep learning approach for object affordance detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "smart mining for deep metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "opengan open set generative adversarial networks")
  Member "faster r cnn features for instance search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deeply activated salient region for instance search")
  Member "fast training of triplet based deep binary embedding networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep heterogeneous hashing for face video retrieval")
  Member "embedding based on function approximation for large scale image search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "simultaneous feature aggregating and hashing for compact binary code learning")

Question: Please tell me which class the source paper 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)