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: "boosted multiple kernel learning for first person activity recognition"
  Abstract: Activity recognition from first-person (ego-centric) videos has recently gained attention due to the increasing ubiquity of the wearable cameras. There has been a surge of efforts adapting existing feature descriptors and designing new descriptors for the first-person videos. An effective activity recognition system requires selection and use of complementary features and appropriate kernels for each feature. In this study, we propose a data-driven framework for first-person activity recognition which effectively selects and combines features and their respective kernels during the training. Our experimental results show that use of Multiple Kernel Learning (MKL) and Boosted MKL in first-person activity recognition problem exhibits improved results in comparison to the state-of-the-art. In addition, these techniques enable the expansion of the framework with new features in an efficient and convenient way.

Connected through hyperedge HE-1 (induced by source paper "three stream fusion network for first person interaction recognition"):
  with members:
    - "robust and discriminative labeling for multi label active learning based on maximum correntropy criterion"
    - "spatiotemporal pyramid network for video action recognition"
    - "non linear temporal subspace representations for activity recognition"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "attentional pooling for action recognition"
    - "convolutional long short term memory networks for recognizing first person interactions"
    - "action recognition with coarse to fine deep feature integration and asynchronous fusion"
    - "going deeper into first person activity recognition"

Connected through hyperedge HE-2 (induced by source paper "multi modal egocentric activity recognition using audio visual features"):
  with members:
    - "convolutional long short term memory networks for recognizing first person interactions"
    - "compact cnn for indexing egocentric videos"
    - "the evolution of first person vision methods a survey"

Connected through hyperedge HE-3 (induced by source paper "convolutional long short term memory networks for recognizing first person interactions"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "sembed semantic embedding of egocentric action videos"
    - "anomaly detection in video using predictive convolutional long short term memory networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "compact cnn for indexing egocentric videos"
    - "spatio temporal video autoencoder with differentiable memory"
    - "unsupervised learning of video representations using lstms"
    - "human action recognition using factorized spatio temporal convolutional networks"

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)