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: "detection bank an object detection based video representation for multimedia event recognition"
  Abstract: While low-level image features have proven to be effective representations for visual recognition tasks such as object recognition and scene classification, they are inadequate to capture complex semantic meaning required to solve high-level visual tasks such as multimedia event detection and recognition. Recognition or retrieval of events and activities can be improved if specific discriminative objects are detected in a video sequence. In this paper, we propose an image representation, called Detection Bank, based on the detection images from a large number of windowed object detectors where an image is represented by different statistics derived from these detections. This representation is extended to video by aggregating the key frame level image representations through mean and max pooling. We empirically show that it captures complementary information to state-of-the-art representations such as Spatial Pyramid Matching and Object Bank. These descriptors combined with our Detection Bank representation significantly outperforms any of the representations alone on TRECVID MED 2011 data.

Connected through hyperedge HE-1 (induced by source paper "action genome actions as composition of spatio temporal scene graphs"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a short note on the kinetics 700 human action dataset"
    - "graphical contrastive losses for scene graph parsing"
    - "protogan towards few shot learning for action recognition"
    - "tarn temporal attentive relation network for few shot and zero shot action recognition"
    - "visual relationships as functions enabling few shot scene graph prediction"
    - "specifying object attributes and relations in interactive scene generation"
    - "scene graph prediction with limited labels"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "long term feature banks for detailed video understanding"):
  with members:
    - "non local netvlad encoding for video classification"
    - "scaling egocentric vision the epic kitchens dataset"
    - "a better baseline for ava"
    - "actor centric relation network"
    - "videos as space time region graphs"
    - "object level visual reasoning in videos"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "iod cnn integrating object detection networks for event recognition"):
  with members:
    - "joint deep exploitation of semantic keywords and visual features for malicious crowd image classification"
    - "object scene convolutional neural networks for event recognition in images"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "dynamic belief fusion for object detection"
    - "better exploiting os cnns for better event recognition in images"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

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.MM(Multimedia)