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 "deep learning for plankton and coral classification"):
  Abstract: Oceans are the essential lifeblood of the Earth: they provide over 70% of the oxygen and over 97% of the water. Plankton and corals are two of the most fundamental components of ocean ecosystems, the former due to their function at many levels of the oceans food chain, the latter because they provide spawning and nursery grounds to many fish populations. Studying and monitoring plankton distribution and coral reefs is vital for environment protection. In the last years there has been a massive proliferation of digital imagery for the monitoring of underwater ecosystems and much research is concentrated on the automated recognition of plankton and corals. In this paper, we present a study about an automated system for monitoring of underwater ecosystems. The system here proposed is based on the fusion of different deep learning methods. We study how to create an ensemble based of different CNN models, fine tuned on several datasets with the aim of exploiting their diversity. The aim of our study is to experiment the possibility of fine-tuning pretrained CNN for underwater imagery analysis, the opportunity of using different datasets for pretraining models, the possibility to design an ensemble using the same architecture with small variations in the training procedure. The experimental results are very encouraging, our experiments performed on 5 well-knowns datasets (3 plankton and 2 coral datasets) show that the fusion of such different CNN models in a heterogeneous ensemble grants a substantial performance improvement with respect to other state-of-the-art approaches in all the tested problems. One of the main contributions of this work is a wide experimental evaluation of famous CNN architectures to report performance of both single CNN and ensemble of CNNs in different problems. Moreover, we show how to create an ensemble which improves the performance of the best single model.

Includes members:
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation":
    also connected through other hyperedges:
      (none)
  Member "underwater fish species classification using convolutional neural network and deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "silhonet fisheye adaptation of a roi based object pose estimation network to monocular fisheye images")
  Member "self normalizing neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "soft root sign activation function")
  Member "learning transferable architectures for scalable image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning curiosity algorithms")
  Member "exploiting cyclic symmetry in convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on learning sets of symmetric elements")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "rethinking the inception architecture for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")

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)