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: "content based filtering for video sharing social networks"
  Abstract: In this paper we compare the use of several features in the task of content filtering for video social networks, a very challenging task, not only because the unwanted content is related to very high-level semantic concepts (e.g., pornography, violence, etc.) but also because videos from social networks are extremely assorted, preventing the use of constrained a priori information. We propose a simple method, able to combine diverse evidence, coming from different features and various video elements (entire video, shots, frames, keyframes, etc.). We evaluate our method in three social network applications, related to the detection of unwanted content - pornographic videos, violent videos, and videos posted to artificially manipulate popularity scores. Using challenging test databases, we show that this simple scheme is able to obtain good results, provided that adequate features are chosen. Moreover, we establish a representation using codebooks of spatiotemporal local descriptors as critical to the success of the method in all three contexts. This is consequential, since the state-of-the-art still relies heavily on static features for the tasks addressed.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal cnns for pornography detection in videos"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "a mid level video representation based on binary descriptors a case study for pornography detection"
    - "applying deep learning to classify pornographic images and videos"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-2 (induced by source paper "a mid level video representation based on binary descriptors a case study for pornography detection"):
  with members:
    - "hashing on nonlinear manifolds"
    - "applying deep learning to classify pornographic images and 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)