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: "few shot video classification via temporal alignment"
  Abstract: There is a growing interest in learning a model which could recognize novel classes with only a few labeled examples. In this paper, we propose Temporal Alignment Module (TAM), a novel few-shot learning framework that can learn to classify a previous unseen video. While most previous works neglect long-term temporal ordering information, our proposed model explicitly leverages the temporal ordering information in video data through temporal alignment. This leads to strong data-efficiency for few-shot learning. In concrete, TAM calculates the distance value of query video with respect to novel class proxies by averaging the per frame distances along its alignment path. We introduce continuous relaxation to TAM so the model can be learned in an end-to-end fashion to directly optimize the few-shot learning objective. We evaluate TAM on two challenging real-world datasets, Kinetics and Something-Something-V2, and show that our model leads to significant improvement of few-shot video classification over a wide range of competitive baselines.

Connected through hyperedge HE-1 (induced by source paper "something else compositional action recognition with spatial temporal interaction networks"):
  with members:
    - "spatio temporal action graph networks"
    - "meta learning with differentiable convex optimization"
    - "a closer look at few shot classification"
    - "reasoning about human object interactions through dual attention networks"
    - "long term feature banks for detailed video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "actor centric relation network"
    - "video action transformer network"

Connected through hyperedge HE-2 (induced by source paper "self paced video data augmentation with dynamic images generated by generative adversarial networks"):
  with members:
    - "tganv2 efficient training of large models for video generation with multiple subsampling layers"
    - "recycle gan unsupervised video retargeting"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "spectral normalization for generative adversarial networks"
    - "towards high resolution video generation with progressive growing of sliced wasserstein gans"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "hidden two stream convolutional networks for action recognition"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-3 (induced by source paper "metric based few shot learning for video action recognition"):
  with members:
    - "a short note about kinetics 600"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "prototypical networks for few shot learning"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "learning to compare relation network for few shot learning"
    - "matching networks for one shot learning"
    - "temporal segment networks towards good practices for deep action recognition"

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)