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: "learning latent super events to detect multiple activities in videos"
  Abstract: In this paper, we introduce the concept of learning latent \emph{super-events} from activity videos, and present how it benefits activity detection in continuous videos. We define a super-event as a set of multiple events occurring together in videos with a particular temporal organization; it is the opposite concept of sub-events. Real-world videos contain multiple activities and are rarely segmented (e.g., surveillance videos), and learning latent super-events allows the model to capture how the events are temporally related in videos. We design \emph{temporal structure filters} that enables the model to focus on particular sub-intervals of the videos, and use them together with a soft attention mechanism to learn representations of latent super-events. Super-event representations are combined with per-frame or per-segment CNNs to provide frame-level annotations. Our approach is designed to be fully differentiable, enabling an end-to-end learning of latent super-event representations jointly with the activity detector using them. Our experiments with multiple public video datasets confirm that the proposed concept of latent super-event learning significantly benefits activity detection, advancing the state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "neural message passing on hybrid spatio temporal visual and symbolic graphs for video understanding"):
  with members:
    - "dynamic graph modules for modeling higher order interactions in activity recognition"
    - "iterative visual reasoning beyond convolutions"
    - "structured label inference for visual understanding"
    - "actor centric relation network"
    - "video action transformer network"
    - "hybrid knowledge routed modules for large scale object detection"
    - "a structured model for action detection"
    - "learning human object interactions by graph parsing neural networks"

Connected through hyperedge HE-2 (induced by source paper "gaussian temporal awareness networks for action localization"):
  with members:
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "soft nms improving object detection with one line of code"
    - "focal loss for dense object detection"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"

Connected through hyperedge HE-3 (induced by source paper "learning to anonymize faces for privacy preserving action detection"):
  with members:
    - "spatially transformed adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "sphereface deep hypersphere embedding for face recognition"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "privacy preserving visual learning using doubly permuted homomorphic encryption"

Connected through hyperedge HE-4 (induced by source paper "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to separate object sounds by watching unlabeled video"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "instance similarity deep hashing for multi label image retrieval"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "single shot temporal action 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)