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 video retrieval in historical collections of the german broadcasting archive"
  Abstract: The German Broadcasting Archive (DRA) maintains the cultural heritage of radio and television broadcasts of the former German Democratic Republic (GDR). The uniqueness and importance of the video material stimulates a large scientific interest in the video content. In this paper, we present an automatic video analysis and retrieval system for searching in historical collections of GDR television recordings. It consists of video analysis algorithms for shot boundary detection, concept classification, person recognition, text recognition and similarity search. The performance of the system is evaluated from a technical and an archival perspective on 2,500 h of GDR television recordings.

Connected through hyperedge HE-1 (induced by source paper "investigating correlations of inter coder agreement and machine annotation performance for historical video data"):
  with members:
    - "vggface2 a dataset for recognising faces across pose and age"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "facenet a unified embedding for face recognition and clustering"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "convolutional hashing for automated scene matching"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "deep hashing with category mask for fast video retrieval"
    - "deep hyperspherical learning"
    - "ridiculously fast shot boundary detection with fully convolutional neural networks"
    - "riemannian approach to batch normalization"
    - "hashnet deep learning to hash by continuation"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "transfer adversarial hashing for hamming space retrieval"

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.DL(Digital Libraries)