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: "objects2action classifying and localizing actions without any video example"
  Abstract: The goal of this paper is to recognize actions in video without the need for examples. Different from traditional zero-shot approaches we do not demand the design and specification of attribute classifiers and class-to-attribute mappings to allow for transfer from seen classes to unseen classes. Our key contribution is objects2action, a semantic word embedding that is spanned by a skip-gram model of thousands of object categories. Action labels are assigned to an object encoding of unseen video based on a convex combination of action and object affinities. Our semantic embedding has three main characteristics to accommodate for the specifics of actions. First, we propose a mechanism to exploit multiple-word descriptions of actions and objects. Second, we incorporate the automated selection of the most responsive objects per action. And finally, we demonstrate how to extend our zero-shot approach to the spatio-temporal localization of actions in video. Experiments on four action datasets demonstrate the potential of our approach.

Connected through hyperedge HE-1 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "semantically aligned bias reducing zero shot learning"
    - "out of distribution detection for generalized zero shot action recognition"
    - "transferable contrastive network for generalized zero shot learning"
    - "feedback network for image super resolution"
    - "generative dual adversarial network for generalized zero shot learning"
    - "towards universal representation for unseen action recognition"
    - "generalized zero and few shot learning via aligned variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "pointly supervised action localization"):
  with members:
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "extreme clicking for efficient object annotation"
    - "localizing actions from video labels and pseudo annotations"
    - "action tubelet detector for spatio temporal action localization"
    - "spatio temporal action detection with cascade proposal and location anticipation"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "spatial aware object embeddings for zero shot localization and classification of actions"
    - "pathtrack fast trajectory annotation with path supervision"

Connected through hyperedge HE-3 (induced by source paper "action recognition in untrimmed videos with composite self attention two stream framework"):
  with members:
    - "learning transferable self attentive representations for action recognition in untrimmed videos with weak supervision"
    - "end to end learning of motion representation for video understanding"
    - "towards universal representation for unseen action recognition"
    - "transductive unbiased embedding for zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "rekall specifying video events using compositions of spatiotemporal labels"):
  with members:
    - "fast video shot transition localization with deep structured models"
    - "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"
    - "blazeit fast exploratory video queries using neural networks"
    - "scanner efficient video analysis at scale"
    - "fine grained retrieval of sports plays using tree based alignment of trajectories"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "prototypical networks for few shot learning"
    - "inferring generative model structure with static analysis"

Connected through hyperedge HE-5 (induced by source paper "semantic fisher scores for task transfer using objects to classify scenes"):
  with members:
    - "locally supervised deep hybrid model for scene recognition"
    - "compositional model based fisher vector coding for image classification"
    - "label embedding for image classification"
    - "deep filter banks for texture recognition description and segmentation"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "probabilistic semantic retrieval for surveillance videos with activity graphs"):
  with members:
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"
    - "simple online and realtime tracking with a deep association metric"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "simple online and realtime tracking"
    - "fast zero shot image tagging"
    - "person re identification by discriminative selection in video ranking"
    - "evaluating two stream cnn for video classification"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "zero shot sign language recognition can textual data uncover sign languages"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "towards universal representation for unseen action recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fine grained object recognition and zero shot learning in remote sensing imagery"
    - "semantic autoencoder for zero shot learning"
    - "alternative semantic representations for zero shot human action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "zero shot recognition of complex action sequences"):
  with members:
    - "action2vec a crossmodal embedding approach to action learning"
    - "informed democracy voting based novelty detection for action recognition"
    - "a proposal based solution to spatio temporal action detection in untrimmed videos"
    - "visual data synthesis via gan for zero shot video classification"
    - "cross modal and hierarchical modeling of video and text"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "what actions are needed for understanding human actions in videos"
    - "r c3d region convolutional 3d network for temporal activity detection"

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)