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 systems"
  Abstract: With the development of multimedia data types and available bandwidth there is huge demand of video retrieval systems, as users shift from text based retrieval systems to content based retrieval systems. Selection of extracted features play an important role in content based video retrieval regardless of video attributes being under consideration. These features are intended for selecting, indexing and ranking according to their potential interest to the user. Good features selection also allows the time and space costs of the retrieval process to be reduced. This survey reviews the interesting features that can be extracted from video data for indexing and retrieval along with similarity measurement methods. We also identify present research issues in area of content based video retrieval systems.

Connected through hyperedge HE-1 (induced by source paper "effects of distance measure choice on k nearest neighbor classifier performance a review"):
  with members:
    - "on enhancing the performance of nearest neighbour classifiers using hassanat distance metric"
    - "solving the problem of the k parameter in the knn classifier using an ensemble learning approach"
    - "dimensionality invariant similarity measure"
    - "experiments of distance measurements in a foliage plant retrieval system"
    - "survey of nearest neighbor techniques"

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"

Connected through hyperedge HE-3 (induced by source paper "a novel approach for shot boundary detection in videos"):
  with members:
    - "video shot boundary detection using motion activity descriptor"
    - "a tutorial on spectral 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.MM(Multimedia)