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: "marginalized average attentional network for weakly supervised learning"
  Abstract: In weakly-supervised temporal action localization, previous works have failed to locate dense and integral regions for each entire action due to the overestimation of the most salient regions. To alleviate this issue, we propose a marginalized average attentional network (MAAN) to suppress the dominant response of the most salient regions in a principled manner. The MAAN employs a novel marginalized average aggregation (MAA) module and learns a set of latent discriminative probabilities in an end-to-end fashion. MAA samples multiple subsets from the video snippet features according to a set of latent discriminative probabilities and takes the expectation over all the averaged subset features. Theoretically, we prove that the MAA module with learned latent discriminative probabilities successfully reduces the difference in responses between the most salient regions and the others. Therefore, MAAN is able to generate better class activation sequences and identify dense and integral action regions in the videos. Moreover, we propose a fast algorithm to reduce the complexity of constructing MAA from O($2^T$) to O($T^2$). Extensive experiments on two large-scale video datasets show that our MAAN achieves superior performance on weakly-supervised temporal action localization

Connected through hyperedge HE-1 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks"):
  with members:
    - "dynamic graph modules for modeling higher order interactions in activity recognition"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "w talc weakly supervised temporal activity localization and classification"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "relational inductive biases deep learning and graph networks"
    - "dynamic graph modules for modeling object object interactions in activity recognition"
    - "a structured model for action detection"
    - "temporal gaussian mixture layer for videos"

Connected through hyperedge HE-2 (induced by source paper "not all attention is needed gated attention network for sequence data"):
  with members:
    - "star transformer"
    - "generating long sequences with sparse transformers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "gaternet dynamic filter selection in convolutional neural network via a dedicated global gating network"
    - "structured attention networks"
    - "learning structured text representations"
    - "attention is all you need"
    - "convolutional networks with adaptive inference graphs"

Connected through hyperedge HE-3 (induced by source paper "background suppression network for weakly supervised temporal action localization"):
  with members:
    - "gaussian temporal awareness networks for action localization"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-4 (induced by source paper "curriculum loss robust learning and generalization against label corruption"):
  with members:
    - "how does disagreement help generalization against label corruption"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "masking a new perspective of noisy supervision"
    - "learning from noisy singly labeled data"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "understanding deep learning requires rethinking generalization"

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.CV(Computer Vision and Pattern Recognition)