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 "social behavioral phenotyping of drosophila with a2d 3d hybrid cnn framework"):
  Abstract: Behavioural phenotyping of Drosophila is an important means in biological and medical research to identify genetic, pathologic or psychologic impact on animal behaviour.

Includes members:
  Member "object detection with deep learning a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "cascade r cnn delving into high quality object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "proposal learning for semi supervised object detection")
  Member "real time action detection in video surveillance using sub action descriptor with multi cnn":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a novel approach for robust multi human action detection and recognition based on 3 dimentional convolutional neural networks")
  Member "spatio temporal human action localisation and instance segmentation in temporally untrimmed videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "why can t i dance in the mall learning to mitigate scene bias in action recognition")
  Member "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "you only look once unified real time object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "beyond short snippets deep networks for video classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")

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)