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 "s dod cnn doubly injecting spatially preserved object information for event recognition"):
  Abstract: We present a novel event recognition approach called Spatially-preserved Doubly-injected Object Detection CNN (S-DOD-CNN), which incorporates the spatially preserved object detection information in both a direct and an indirect way. Indirect injection is carried out by simply sharing the weights between the object detection modules and the event recognition module. Meanwhile, our novelty lies in the fact that we have preserved the spatial information for the direct injection. Once multiple regions-of-intereset (RoIs) are acquired, their feature maps are computed and then projected onto a spatially-preserving combined feature map using one of the four RoI Projection approaches we present. In our architecture, combined feature maps are generated for object detection which are directly injected to the event recognition module. Our method provides the state-of-the-art accuracy for malicious event recognition.

Includes members:
  Member "dod cnn doubly injecting object information for event recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "is pretraining necessary for hyperspectral image classification")
  Member "iod cnn integrating object detection networks for event recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "is pretraining necessary for hyperspectral image classification")
  Member "enhanced object detection via fusion with prior beliefs from image classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dod cnn doubly injecting object information for event recognition")
  Member "exploitation of semantic keywords for malicious event classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object and text guided semantics for cnn based activity recognition")
  Member "object scene convolutional neural networks for event recognition in images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph neural networks for image understanding based on multiple cues group emotion recognition and event recognition as use cases")
  Member "dynamic belief fusion for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "alfa agglomerative late fusion algorithm for object detection")
  Member "better exploiting os cnns for better event recognition in images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "iod cnn integrating object detection networks for event recognition")
  Member "detection bank an object detection based video representation for multimedia event recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "action genome actions as composition of spatio temporal scene graphs")

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)