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: "iod cnn integrating object detection networks for event recognition"
  Abstract: Many previous methods have showed the importance of considering semantically relevant objects for performing event recognition, yet none of the methods have exploited the power of deep convolutional neural networks to directly integrate relevant object information into a unified network. We present a novel unified deep CNN architecture which integrates architecturally different, yet semantically-related object detection networks to enhance the performance of the event recognition task. Our architecture allows the sharing of the convolutional layers and a fully connected layer which effectively integrates event recognition, rigid object detection and non-rigid object detection.

Connected through hyperedge HE-1 (induced by source paper "s dod cnn doubly injecting spatially preserved object information for event recognition"):
  with members:
    - "dod cnn doubly injecting object information 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"
    - "dod cnn doubly injecting object information for event recognition"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "contextual deep cnn based hyperspectral classification"

Connected through hyperedge HE-3 (induced by source paper "object and text guided semantics for cnn based activity recognition"):
  with members:
    - "incorporating subword information into matrix factorization word embeddings"
    - "exploitation of semantic keywords for malicious event classification"
    - "temporal segment networks towards good practices for deep action recognition"
    - "bag of tricks for efficient text classification"
    - "enhancing the lexvec distributed word representation model using positional contexts and external memory"
    - "an all in one convolutional neural network for face analysis"
    - "deep residual learning for image recognition"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "dod cnn doubly injecting object information for event recognition"):
  with members:
    - "learning to detect human object interactions"
    - "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"
    - "detection bank an object detection based video representation for multimedia event recognition"

Connected through hyperedge HE-5 (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"
    - "dod cnn doubly injecting object information for event recognition"
    - "detecting and recognizing human object interactions"

Connected through hyperedge HE-6 (induced by source paper "joint deep exploitation of semantic keywords and visual features for malicious crowd image classification"):
  with members:
    - "weakly supervised localization using deep feature maps"
    - "top down neural attention by excitation backprop"
    - "deep residual learning for image recognition"
    - "dynamic belief fusion for object detection"

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)