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: "zero shot recognition of complex action sequences"
  Abstract: Zero-shot video classification for fine-grained activity recognition has largely been explored using methods similar to its image-based counterpart, namely by defining image-derived attributes that serve to discriminate among classes. However, such methods do not capture the fundamental dynamics of activities and are thus limited to cases where static image content alone suffices to classify an activity. For example, reversible actions such as entering and exiting a car are often indistinguishable. #R##N#In this work, we present a framework for straightforward modeling of activities as a state machine of dynamic attributes. We show that encoding the temporal structure of attributes greatly increases our modeling power, allowing us to capture action direction, for example. Further, we can extend this to activity detection using dynamic programming, providing, to our knowledge, the first example of zero-shot joint segmentation and classification of complex action sequences in a larger video. #R##N#We evaluate our method on the Olympic Sports dataset where our model establishes a new state of the art for standard zero-shot-learning (ZSL) evaluation as well as outperforming all other models in the inductive category for general (GZSL) zero-shot evaluation. Additionally, we are the first to demonstrate zero-shot decoding of complex action sequences on a widely used surgical dataset. Lastly, we show that that we can even eliminate the need to train attribute detectors by using off-the-shelf object detectors to recognize activities in challenging surveillance videos.

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)