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: "spatial aware object embeddings for zero shot localization and classification of actions"
  Abstract: We aim for zero-shot localization and classification of human actions in video. Where traditional approaches rely on global attribute or object classification scores for their zero-shot knowledge transfer, our main contribution is a spatial-aware object embedding. To arrive at spatial awareness, we build our embedding on top of freely available actor and object detectors. Relevance of objects is determined in a word embedding space and further enforced with estimated spatial preferences. Besides local object awareness, we also embed global object awareness into our embedding to maximize actor and object interaction. Finally, we exploit the object positions and sizes in the spatial-aware embedding to demonstrate a new spatio-temporal action retrieval scenario with composite queries. Action localization and classification experiments on four contemporary action video datasets support our proposal. Apart from state-of-the-art results in the zero-shot localization and classification settings, our spatial-aware embedding is even competitive with recent supervised action localization alternatives.

Connected through hyperedge HE-1 (induced by source paper "learning spatiotemporal features via video and text pair discrimination"):
  with members:
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "contrastive multiview coding"
    - "universal dependency parsing from scratch"
    - "dynamonet dynamic action and motion network"
    - "learning correspondence from the cycle consistency of time"
    - "videobert a joint model for video and language representation learning"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"

Connected through hyperedge HE-2 (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-3 (induced by source paper "domain specific priors and meta learning for low shot first person action recognition"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "what have we learned from deep representations for action recognition"
    - "how to train your maml"
    - "towards an unequivocal representation of actions"
    - "probabilistic model agnostic meta learning"
    - "object level visual reasoning in videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-4 (induced by source paper "spatio temporal action graph networks"):
  with members:
    - "charades ego a large scale dataset of paired third and first person videos"
    - "referring relationships"
    - "relational deep reinforcement learning"
    - "graph networks as learnable physics engines for inference and control"
    - "actor centric relation network"
    - "video action transformer network"
    - "relational inductive biases deep learning and graph networks"
    - "videos as space time region graphs"

Connected through hyperedge HE-5 (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"
    - "pathtrack fast trajectory annotation with path supervision"
    - "tube convolutional neural network t cnn for action detection in videos"

Connected through hyperedge HE-6 (induced by source paper "zero shot action recognition in videos a survey"):
  with members:
    - "action2vec a crossmodal embedding approach to action learning"
    - "human action recognition and prediction a survey"
    - "fine grained activity recognition in baseball videos"
    - "towards universal representation for unseen action recognition"
    - "a short note about kinetics 600"
    - "graph convolutional networks for text classification"
    - "visual data synthesis via gan for zero shot video classification"
    - "cross modal and hierarchical modeling of video and text"

Connected through hyperedge HE-7 (induced by source paper "dazsl dynamic attributes for zero shot learning"):
  with members:
    - "out of distribution detection for generalized zero shot action recognition"
    - "zero shot action recognition in videos a survey"
    - "action2vec a crossmodal embedding approach to action learning"
    - "informed democracy voting based novelty detection for action recognition"
    - "visual data synthesis via gan for zero shot video classification"
    - "tsm temporal shift module for efficient video understanding"
    - "cross modal and hierarchical modeling of video and text"
    - "r c3d region convolutional 3d network for temporal activity detection"

Connected through hyperedge HE-8 (induced by source paper "a structured model for action detection"):
  with members:
    - "towards segmenting everything that moves"
    - "actor centric relation network"
    - "learning human object interactions by graph parsing neural networks"
    - "videos as space time region graphs"
    - "object level visual reasoning in videos"
    - "detecting and recognizing human object interactions"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "in defense of the triplet loss for person re identification"

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)