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: "vectors of locally aggregated centers for compact video representation"
  Abstract: We propose a novel vector aggregation technique for compact video representation, with application in accurate similarity detection within large video datasets. The current state-of-the-art in visual search is formed by the vector of locally aggregated descriptors (VLAD) of Jegou et al. VLAD generates compact video representations based on scale-invariant feature transform (SIFT) vectors (extracted per frame) and local feature centers computed over a training set. With the aim to increase robustness to visual distortions, we propose a new approach that operates at a coarser level in the feature representation. We create vectors of locally aggregated centers (VLAC) by first clustering SIFT features to obtain local feature centers (LFCs) and then encoding the latter with respect to given centers of local feature centers (CLFCs), extracted from a training set. The sum-of-differences between the LFCs and the CLFCs are aggregated to generate an extremely-compact video description used for accurate video segment similarity detection. Experimentation using a video dataset, comprising more than 1000 minutes of content from the Open Video Project, shows that VLAC obtains substantial gains in terms of mean Average Precision (mAP) against VLAD and the hyper-pooling method of Douze et al., under the same compaction factor and the same set of distortions.

Connected through hyperedge HE-1 (induced by source paper "dithen a computation as a service cloud platform for large scale multimedia processing"):
  with members:
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-2 (induced by source paper "encoding the local connectivity patterns of fmri for cognitive state classification"):
  with members:
    - "joint recognition and segmentation of actions via probabilistic integration of spatio temporal fisher vectors"
    - "learning discriminative bayesian networks from high dimensional continuous neuroimaging data"
    - "encoding high dimensional local features by sparse coding based fisher vectors"
    - "bag of words representation for biomedical time series classification"
    - "mesh learning for classifying cognitive processes"

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)