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 camera action dataset mcad a dataset for studying non overlapped cross camera action recognition"
  Abstract: Action recognition has received increasing attentions from the computer vision and machine learning communities in the last decades. To enable the study of this problem, there exist a vast number of action datasets, which are recorded under the controlled laboratory environment, the real-world surveillance environment, or crawled from the Internet. Most of these datasets are recorded using one camera, or simultaneously recorded with the same action with multiple overlapped cameras. This results in that the training and test data are often resemble similar environment conditions. In this paper, we introduce a new dataset, namely Multi-Camera Action Dataset (MCAD), which is designed to evaluate the open view classification problem under the surveillance environment. Specifically, it enable researchers to study the robustness of exiting algorithm for actions that are recorded from multiple cameras with similar Field-of-View (FOV) and environment conditions. This dataset contains 14,298 action samples from 18 action categories, including 9 classes of single person actions and 9 classes of person-object actions, which are performed by 20 subjects and recorded with 5 cameras. Inspired by the well received evaluation approach on the LFW dataset, we design a standard evaluation protocol to enable comparisonagainst existing works.

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)