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 "fine grained classification using heterogeneous web data and auxiliary categories"):
  Abstract: Fine-grained classification remains a very challenging problem, because of the absence of well-labeled training data caused by the high cost of annotating a large number of fine-grained categories. In the extreme case, given a set of test categories without any well-labeled training data, the majority of existing works can be grouped into the following two research directions: 1) crawl noisy labeled web data for the test categories as training data, which is dubbed as webly supervised learning; 2) transfer the knowledge from auxiliary categories with well-labeled training data to the test categories, which corresponds to zero-shot learning setting. Nevertheless, the above two research directions still have critical issues to be addressed. For the first direction, web data have noisy labels and considerably different data distribution from test data. For the second direction, zero-shot learning is struggling to achieve compelling results compared with conventional supervised learning. The issues of the above two directions motivate us to develop a novel approach which can jointly exploit both noisy web training data from test categories and well-labeled training data from auxiliary categories. In particular, on one hand, we crawl web data for test categories as noisy training data. On the other hand, we transfer the knowledge from auxiliary categories with well-labeled training data to test categories by virtue of free semantic information (e.g., word vector) of all categories. Moreover, given the fact that web data are generally associated with additional textual information (e.g., title and tag), we extend our method by using the surrounding textual information of web data as privileged information. Extensive experiments show the effectiveness of our proposed methods.

Includes members:
  Member "zero shot kernel learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "from anchor generation to distribution alignment learning a discriminative embedding space for zero shot recognition")
  Member "learning from noisy web data with category level supervision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi class classification from noisy similarity labeled data")
  Member "miml fcn multi instance multi label learning via fully convolutional networks with privileged information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hard pixel mining for depth privileged semantic segmentation")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "zero shot learning the good the bad and the ugly":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crl class representative learning for image classification")
  Member "semi supervised zero shot learning by a clustering based approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an optimal transport framework for zero shot learning")
  Member "unsupervised pixel level domain adaptation with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "attend in groups a weakly supervised deep learning framework for learning from web data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning from web data with self organizing memory module")

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)