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: "exploitation of semantic keywords for malicious event classification"
  Abstract: Learning an event classifier is challenging when the scenes are semantically different but visually similar. However, as humans, we typically handle such tasks painlessly by adding our background semantic knowledge. Motivated by this observation, we aim to provide an empirical study about how additional information such as semantic keywords can boost up the discrimination of such events. To demonstrate the validity of this study, we first construct a novel Malicious Crowd Dataset containing crowd images with two events, benign and malicious, which look visually similar. Note that the primary focus of this paper is not to provide the state-of-the-art performance on this dataset but to show the beneficial aspects of using semantically-driven keyword information. By leveraging crowd-sourcing platforms, such as Amazon Mechanical Turk, we collect semantic keywords associated with images and then subsequently identify a subset of keywords (e.g. police, fire, etc.) unique to specific events. We first show that by using recently introduced attention models, a naive CNN-based event classifier actually learns to primarily focus on local attributes associated with the discriminant semantic keywords identified by the Turks. We further show that incorporating the keyword-driven information into early- and late-fusion approaches can significantly enhance malicious event classification.

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"
    - "iod cnn integrating object detection networks for event recognition"
    - "enhanced object detection via fusion with prior beliefs from image 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 "object and text guided semantics for cnn based activity recognition"):
  with members:
    - "incorporating subword information into matrix factorization word embeddings"
    - "iod cnn integrating object detection networks for event recognition"
    - "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-3 (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"
    - "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-4 (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"

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)