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: "predicting action tubes"
  Abstract: In this work, we present a method to predict an entire `action tube' (a set of temporally linked bounding boxes) in a trimmed video just by observing a smaller subset of it. Predicting where an action is going to take place in the near future is essential to many computer vision based applications such as autonomous driving or surgical robotics. Importantly, it has to be done in real-time and in an online fashion. We propose a Tube Prediction network (TPnet) which jointly predicts the past, present and future bounding boxes along with their action classification scores. At test time TPnet is used in a (temporal) sliding window setting, and its predictions are put into a tube estimation framework to construct/predict the video long action tubes not only for the observed part of the video but also for the unobserved part. Additionally, the proposed action tube predictor helps in completing action tubes for unobserved segments of the video. We quantitatively demonstrate the latter ability, and the fact that TPnet improves state-of-the-art detection performance, on one of the standard action detection benchmarks - J-HMDB-21 dataset.

Connected through hyperedge HE-1 (induced by source paper "you only watch once a unified cnn architecture for real time spatiotemporal action localization"):
  with members:
    - "resource efficient 3d convolutional neural networks"
    - "step spatio temporal progressive learning for video action detection"
    - "eco efficient convolutional network for online video understanding"
    - "videocapsulenet a simplified network for action detection"
    - "cbam convolutional block attention module"
    - "dual attention network for scene segmentation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning representations for predicting future activities"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "stochastic adversarial video prediction"
    - "when will you do what anticipating temporal occurrences of activities"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "predicting deeper into the future of semantic segmentation"
    - "on calibration of modern neural networks"
    - "contextvp fully context aware video prediction"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal action localization in a weakly supervised setting"):
  with members:
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "incremental tube construction for human action detection"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "object detection from video tubelets with convolutional neural networks"
    - "deep learning for detecting multiple space time action tubes in videos"
    - "towards weakly supervised action localization"
    - "automatic action annotation in weakly labeled videos"
    - "relaxed multiple instance svm with application to object discovery"

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)