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: "understanding patch based learning by explaining predictions"
  Abstract: Deep networks are able to learn highly predictive models of video data. Due to video length, a common strategy is to train them on small video snippets. We apply the deep Taylor / LRP technique to understand the deep network's classification decisions, and identify a "border effect": a tendency of the classifier to look mainly at the bordering frames of the input. This effect relates to the step size used to build the video snippet, which we can then tune in order to improve the classifier's accuracy without retraining the model. To our knowledge, this is the the first work to apply the deep Taylor / LRP technique on any video analyzing neural network.

Connected through hyperedge HE-1 (induced by source paper "class feature pyramids for video explanation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "video classification with channel separated convolutional networks"
    - "learning spatio temporal representation with local and global diffusion"
    - "saliency tubes visual explanations for spatio temporal convolutions"
    - "large scale weakly supervised pre training for video action recognition"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "multigrain a unified image embedding for classes and instances"
    - "grounding visual explanations"

Connected through hyperedge HE-2 (induced by source paper "analysis of video feature learning in two stream cnns on the example of zebrafish swim bout classification"):
  with members:
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "visualizing convolutional neural networks to improve decision support for skin lesion classification"
    - "network dissection quantifying interpretability of deep visual representations"
    - "the devil is in the tails fine grained classification in the wild"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

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.LG(Machine Learning)