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: "multi label class imbalanced action recognition in hockey videos via 3d convolutional neural networks"
  Abstract: Automatic analysis of the video is one of most complex problems in the fields of computer vision and machine learning. A significant part of this research deals with (human) activity recognition (HAR) since humans, and the activities that they perform, generate most of the video semantics. Video-based HAR has applications in various domains, but one of the most important and challenging is HAR in sports videos. Some of the major issues include high inter- and intra-class variations, large class imbalance, the presence of both group actions and single player actions, and recognizing simultaneous actions, i.e., the multi-label learning problem. Keeping in mind these challenges and the recent success of CNNs in solving various computer vision problems, in this work, we implement a 3D CNN based multi-label deep HAR system for multi-label class-imbalanced action recognition in hockey videos. We test our system for two different scenarios: an ensemble of $k$ binary networks vs. a single $k$-output network, on a publicly available dataset. We also compare our results with the system that was originally designed for the chosen dataset. Experimental results show that the proposed approach performs better than the existing solution.

Connected through hyperedge HE-1 (induced by source paper "synthetic oversampling of multi label data based on local label distribution"):
  with members:
    - "making classifier chains resilient to class imbalance"
    - "dealing with difficult minority labels in imbalanced mutilabel data sets"
    - "remedial hwr tackling multilabel imbalance through label decoupling and data resampling hybridization"
    - "towards label imbalance in multi label classification with many labels"
    - "should we really use post hoc tests based on mean ranks"

Connected through hyperedge HE-2 (induced by source paper "temporal hockey action recognition via pose and optical flows"):
  with members:
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "hidden two stream convolutional networks for action recognition"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "pose for action action for pose"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-3 (induced by source paper "tri axial self attention for concurrent activity recognition"):
  with members:
    - "structural recurrent neural network srnn for group activity analysis"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "concurrent activity recognition with multimodal cnn lstm structure"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "speed accuracy trade offs for modern convolutional object detectors"

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)