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 "discovering and generating hard examples for training a red tide detector"):
  Abstract: Currently, accurate detection of natural phenomena, such as red tide, that adversely affect wildlife and human, using satellite images has been increasingly utilized. However, red tide detection on satellite images still remains a very hard task due to unpredictable nature of red tide occurrence, extreme sparsity of red tide samples, difficulties in accurate groundtruthing, etc. In this paper, we aim to tackle both the data sparsity and groundtruthing issues by primarily addressing two challenges: i) extreme data imbalance between red tide and non-red tide examples and ii) significant lack of hard examples of non-red tide that can enhance detection performance. In the proposed work, we devise a 9-layer fully convolutional network jointly optimized with two plug-in modules tailored to overcoming the two challenges: i) cascaded online hard example mining (cOHEM) to ease the data imbalance and ii) a hard negative example generator (HNG) to supplement the hard negative (non-red tide) examples. Our proposed network jointly trained with cOHEM and HNG provides state-of-the-art red tide detection accuracy on GOCI satellite images.

Includes members:
  Member "deep video generation prediction and completion of human action sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when relation networks meet gans relation gans with triplet loss")
  Member "cross domain cnn for hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "is pretraining necessary for hyperspectral image classification")
  Member "going deeper with contextual cnn for hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for classification of hyperspectral data a comparative review")
  Member "a fast rcnn hard positive generation via adversary for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on leveraging pretrained gans for limited data generation")
  Member "future frame prediction for anomaly detection a new baseline":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "residual conv deconv grid network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "contextual deep cnn based hyperspectral classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep connections between learning from limited labels physical parameter estimation inspiration for regularization")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secure and robust machine learning for healthcare a survey")

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)