human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "encoding the local connectivity patterns of fmri for cognitive state classification"):
  Abstract: In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher Vectors (FV), Vector of Locally Aggregated Descriptors (VLAD) and Bag-of-Words (BoW) methods. We first obtain local descriptors, called Mesh Arc Descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called \textit{brain connectivity dictionary} by fitting a generative Gaussian mixture model (GMM) to a set of MADs, and selecting the codewords at the mean of each component of the mixture. Codewords represent the connectivity patterns among anatomical regions. We also encode MADs by VLAD and BoW methods using the k-Means clustering. #R##N#We classify the cognitive states of Human Connectome Project (HCP) task fMRI dataset, where we train support vector machines (SVM) by the encoded MADs. Results demonstrate that, FV encoding of MADs can be successfully employed for classification of cognitive tasks, and outperform the VLAD and BoW representations. Moreover, we identify the significant Gaussians in mixture models by computing energy of their corresponding FV parts, and analyze their effect on classification accuracy. Finally, we suggest a new method to visualize the codewords of brain connectivity dictionary.

Includes members:
  Member "joint recognition and segmentation of actions via probabilistic integration of spatio temporal fisher vectors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "comparative evaluation of action recognition methods via riemannian manifolds fisher vectors and gmms ideal and challenging conditions")
  Member "learning discriminative bayesian networks from high dimensional continuous neuroimaging data":
    also connected through other hyperedges:
      (none)
  Member "vectors of locally aggregated centers for compact video representation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dithen a computation as a service cloud platform for large scale multimedia processing")
  Member "encoding high dimensional local features by sparse coding based fisher vectors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep cnns meet global covariance pooling better representation and generalization")
  Member "bag of words representation for biomedical time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ts chief a scalable and accurate forest algorithm for time series classification")
  Member "mesh learning for classifying cognitive processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "modeling brain networks with artificial neural networks")

Question: Please tell me which class the source paper 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)