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: "a generative approach to zero shot and few shot action recognition"
  Abstract: We present a generative framework for zero-shot action recognition where some of the possible action classes do not occur in the training data. Our approach is based on modeling each action class using a probability distribution whose parameters are functions of the attribute vector representing that action class. In particular, we assume that the distribution parameters for any action class in the visual space can be expressed as a linear combination of a set of basis vectors where the combination weights are given by the attributes of the action class. These basis vectors can be learned solely using labeled data from the known (i.e., previously seen) action classes, and can then be used to predict the parameters of the probability distributions of unseen action classes. We consider two settings: (1) Inductive setting, where we use only the labeled examples of the seen action classes to predict the unseen action class parameters; and (2) Transductive setting which further leverages unlabeled data from the unseen action classes. Our framework also naturally extends to few-shot action recognition where a few labeled examples from unseen classes are available. Our experiments on benchmark datasets (UCF101, HMDB51 and Olympic) show significant performance improvements as compared to various baselines, in both standard zero-shot (disjoint seen and unseen classes) and generalized zero-shot learning settings.

Connected through hyperedge HE-1 (induced by source paper "few shot video classification via temporal alignment"):
  with members:
    - "a closer look at few shot classification"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "eco efficient convolutional network for online video understanding"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "meta learning with latent embedding optimization"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "videos as space time region graphs"

Connected through hyperedge HE-2 (induced by source paper "protogan towards few shot learning for action recognition"):
  with members:
    - "a closer look at few shot classification"
    - "multi modal cycle consistent generalized zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "prototypical networks for few shot learning"
    - "a simple neural attentive meta learner"
    - "matching networks for one shot learning"
    - "temporal segment networks towards good practices for deep action recognition"
    - "low shot visual object recognition"

Connected through hyperedge HE-3 (induced by source paper "skeleton based zero shot action recognition in joint pose language semantic space"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "learning to compare relation network for few shot learning"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "human action recognition without human"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "p cnn pose based cnn features for action recognition"

Connected through hyperedge HE-4 (induced by source paper "similarity r c3d for few shot temporal activity detection"):
  with members:
    - "autoloc weakly supervised temporal action localization"
    - "visual data synthesis via gan for zero shot video classification"
    - "temporal action detection with structured segment networks"
    - "prototypical networks for few shot learning"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "zero shot activity recognition with verb attribute induction"

Connected through hyperedge HE-5 (induced by source paper "cross modal hallucination for few shot fine grained recognition"):
  with members:
    - "chatpainter improving text to image generation using dialogue"
    - "low shot learning from imaginary data"
    - "low shot learning with large scale diffusion"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "prototypical networks for few shot learning"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "zero shot fine grained classification by deep feature learning with semantics"
    - "vse improving visual semantic embeddings with hard negatives"

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)