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: "generalized zero shot learning for action recognition with web scale video data"
  Abstract: Action recognition in surveillance video makes our life safer by detecting the criminal events or predicting violent emergencies. However, efficient action recognition is not free of difficulty. First, there are so many action classes in daily life that we cannot pre-define all possible action classes beforehand. Moreover, it is very hard to collect real-word videos for certain particular actions such as steal and street fight due to legal restrictions and privacy protection. These challenges make existing data-driven recognition methods insufficient to attain desired performance. Zero-shot learning is potential to be applied to solve these issues since it can perform classification without positive example. Nevertheless, current zero-shot learning algorithms have been studied under the unreasonable setting where seen classes are absent during the testing phase. Motivated by this, we study the task of action recognition in surveillance video under a more realistic \emph{generalized zero-shot setting}, where testing data contains both seen and unseen classes. To our best knowledge, this is the first work to study video action recognition under the generalized zero-shot setting. We firstly perform extensive empirical studies on several existing zero-shot leaning approaches under this new setting on a web-scale video data. Our experimental results demonstrate that, under the generalize setting, typical zero-shot learning methods are no longer effective for the dataset we applied. Then, we propose a method for action recognition by deploying generalized zero-shot learning, which transfers the knowledge of web video to detect the anomalous actions in surveillance videos. To verify the effectiveness of our proposed method, we further construct a new surveillance video dataset consisting of nine action classes related to the public safety situation.

Connected through hyperedge HE-1 (induced by source paper "zero shot action recognition in videos a survey"):
  with members:
    - "action2vec a crossmodal embedding approach to action learning"
    - "human action recognition and prediction a survey"
    - "fine grained activity recognition in baseball videos"
    - "towards universal representation for unseen action recognition"
    - "a short note about kinetics 600"
    - "graph convolutional networks for text classification"
    - "visual data synthesis via gan for zero shot video classification"
    - "cross modal and hierarchical modeling of video and text"

Connected through hyperedge HE-2 (induced by source paper "dazsl dynamic attributes for zero shot learning"):
  with members:
    - "out of distribution detection for generalized zero shot action recognition"
    - "zero shot action recognition in videos a survey"
    - "action2vec a crossmodal embedding approach to action learning"
    - "informed democracy voting based novelty detection for action recognition"
    - "visual data synthesis via gan for zero shot video classification"
    - "tsm temporal shift module for efficient video understanding"
    - "cross modal and hierarchical modeling of video and text"
    - "r c3d region convolutional 3d network for temporal activity detection"

Connected through hyperedge HE-3 (induced by source paper "deep zero shot learning for scene sketch"):
  with members:
    - "learning deep sketch abstraction"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "a zero shot framework for sketch based image retrieval"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "generalized zero shot recognition based on visually semantic embedding"
    - "sketchyscene richly annotated scene sketches"
    - "unsupervised person re identification by deep learning tracklet association"
    - "semantic autoencoder for zero shot learning"

Connected through hyperedge HE-4 (induced by source paper "street scene a new dataset and evaluation protocol for video anomaly detection"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "object centric auto encoders and dummy anomalies for abnormal event detection in video"
    - "detecting abnormal events in video using narrowed normality clusters"
    - "abnormal event detection in videos using generative adversarial nets"
    - "unmasking the abnormal events in video"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"
    - "future frame prediction for anomaly detection a new baseline"
    - "energy based models for video anomaly 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)