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: "inferring human activities using robust privileged probabilistic learning"
  Abstract: Classification models may often suffer from "structure imbalance" between training and testing data that may occur due to the deficient data collection process. This imbalance can be represented by the learning using privileged information (LUPI) paradigm. In this paper, we present a supervised probabilistic classification approach that integrates LUPI into a hidden conditional random field (HCRF) model. The proposed model is called LUPI-HCRF and is able to cope with additional information that is only available during training. Moreover, the proposed method employes Student's t-distribution to provide robustness to outliers by modeling the conditional distribution of the privileged information. Experimental results in three publicly available datasets demonstrate the effectiveness of the proposed approach and improve the state-of-the-art in the LUPI framework for recognizing human activities.

Connected through hyperedge HE-1 (induced by source paper "human activity recognition using robust adaptive privileged probabilistic learning"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "adam a method for stochastic optimization"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "privileged information for data clustering"

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)