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 "internet based image retrieval using end to end trained deep distributions"):
  Abstract: Internet image search engines have long been considered as a promising tool for handling open-vocabulary textual user queries to unannotated image datasets. However, systems that use this tool have to deal with multi-modal and noisy image sets returned by search engines, especially for polysemous queries. Generally, for many queries, only a small part of the returned sets can be relevant to the user intent. #R##N#In this work, we suggest an approach that explicitly accounts for the complex and noisy structure of the image sets returned by Internet image search engines. Similarly to a considerable number of previous image retrieval works, we train a deep convolutional network that maps images to high-dimensional descriptors. To model image sets obtained from the Internet, our approach then fits a simple probabilistic model that accounts for multi-modality and noise (e.g. a Gaussian mixture model) to the deep descriptors of the images in this set. Finally, the resulting distribution model can be used to search in the unannotated image dataset by evaluating likelihoods of individual images. #R##N#As our main contribution, we develop an end-to-end training procedure that tunes the parameters of a deep network using an annotated training set, while accounting for the distribution fitting and the subsequent matching. In the experiments, we show that such an end-to-end approach boosts the accuracy of the Internet-based image retrieval for hold-out concepts, as compared to retrieval systems that fit similar distribution models to pre-trained features and to simpler end-to-end trained baselines.

Includes members:
  Member "learning deep embeddings with histogram loss":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "aggregating deep convolutional features for image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "solar second order loss and attention for image retrieval")
  Member "visual instance retrieval with deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deeply activated salient region for instance search")
  Member "imagenet large scale visual recognition challenge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "neural codes for image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")

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)