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: "pillar networks for action recognition"
  Abstract: Image understanding using deep convolutional network has reached human-level performance, yet a closely related problem of video understanding especially, action recognition has not reached the requisite level of maturity. We combine multi-kernels based support-vector-machines (SVM) with a multi-stream deep convolutional neural network to achieve close to state-of-the-art performance on a 51-class activity recognition problem (HMDB-51 dataset); this specific dataset has proved to be particularly challenging for deep neural networks due to the heterogeneity in camera viewpoints, video quality, etc. The resulting architecture is named pillar networks as each (very) deep neural network acts as a pillar for the hierarchical classifiers.

Connected through hyperedge HE-1 (induced by source paper "pillar networks distributed non parametric deep and wide networks"):
  with members:
    - "discriminatively learned hierarchical rank pooling networks"
    - "the kinetics human action video dataset"
    - "ts lstm and temporal inception exploiting spatiotemporal dynamics for activity recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "temporal segment networks towards good practices for deep action recognition"
    - "youtube 8m a large scale video classification benchmark"
    - "convolutional two stream network fusion for video action recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "differential geometric retrieval of deep features"):
  with members:
    - "large scale image analysis using docker sandboxing"
    - "pillar networks distributed non parametric deep and wide networks"
    - "wide deep learning for recommender systems"
    - "understanding deep image representations by inverting them"
    - "how transferable are features in deep neural networks"
    - "return of the devil in the details delving deep into convolutional nets"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "describing textures in the wild"

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)