human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "dod cnn doubly injecting object information for event recognition"
  Abstract: Recognizing an event in an image can be enhanced by detecting relevant objects in two ways: 1) indirectly utilizing object detection information within the unified architecture or 2) directly making use of the object detection output results. We introduce a novel approach, referred to as Doubly-injected Object Detection CNN (DOD-CNN), exploiting the object information in both ways for the task of event recognition. The structure of this network is inspired by the Integrated Object Detection CNN (IOD-CNN) where object information is indirectly exploited by the event recognition module through the shared portion of the network. In the DOD-CNN architecture, the intermediate object detection outputs are directly injected into the event recognition network while keeping the indirect sharing structure inherited from the IOD-CNN, thus being `doubly-injected'. We also introduce a batch pooling layer which constructs one representative feature map from multiple object hypotheses. We have demonstrated the effectiveness of injecting the object detection information in two different ways in the task of malicious event recognition.

Connected through hyperedge HE-1 (induced by source paper "s dod cnn doubly injecting spatially preserved object information for event recognition"):
  with members:
    - "iod cnn integrating object detection networks for event recognition"
    - "enhanced object detection via fusion with prior beliefs from image classification"
    - "exploitation of semantic keywords for malicious event classification"
    - "object scene convolutional neural networks for event recognition in images"
    - "dynamic belief fusion for object detection"
    - "better exploiting os cnns for better event recognition in images"
    - "detection bank an object detection based video representation for multimedia event recognition"

Connected through hyperedge HE-2 (induced by source paper "is pretraining necessary for hyperspectral image classification"):
  with members:
    - "rethinking imagenet pre training"
    - "exploring the limits of weakly supervised pretraining"
    - "cross domain cnn for hyperspectral image classification"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "iod cnn integrating object detection networks for event recognition"
    - "contextual deep cnn based hyperspectral classification"

Connected through hyperedge HE-3 (induced by source paper "me r cnn multi expert r cnn for object detection"):
  with members:
    - "s dod cnn doubly injecting spatially preserved object information for event recognition"
    - "growing a brain fine tuning by increasing model capacity"
    - "is pretraining necessary for hyperspectral image classification"
    - "yolov3 an incremental improvement"
    - "cross domain cnn for hyperspectral image classification"
    - "path aggregation network for instance segmentation"
    - "detecting and recognizing human object interactions"
    - "soft nms improving object detection with one line of code"

Question: Please tell me which class the center node 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)