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: "action2vec a crossmodal embedding approach to action learning"
  Abstract: We describe a novel cross-modal embedding space for actions, named Action2Vec, which combines linguistic cues from class labels with spatio-temporal features derived from video clips. Our approach uses a hierarchical recurrent network to capture the temporal structure of video features. We train our embedding using a joint loss that combines classification accuracy with similarity to Word2Vec semantics. We evaluate Action2Vec by performing zero shot action recognition and obtain state of the art results on three standard datasets. In addition, we present two novel analogy tests which quantify the extent to which our joint embedding captures distributional semantics. This is the first joint embedding space to combine verbs and action videos, and the first to be thoroughly evaluated with respect to its distributional semantics.

Connected through hyperedge HE-1 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications"):
  with members:
    - "revisiting training strategies and generalization performance in deep metric learning"
    - "action recognition with spatial temporal discriminative filter banks"
    - "divide and conquer the embedding space for metric learning"
    - "cross and learn cross modal self supervision"
    - "informed democracy voting based novelty detection for action recognition"
    - "visual data synthesis via gan for zero shot video classification"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "sniper efficient multi scale training"

Connected through hyperedge HE-2 (induced by source paper "fine grained action retrieval through multiple parts of speech embeddings"):
  with members:
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "higher order network for action recognition"
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "on the effectiveness of task granularity for transfer learning"
    - "cross modal embeddings for video and audio retrieval"
    - "predicting visual features from text for image and video caption retrieval"

Connected through hyperedge HE-3 (induced by source paper "zero shot action recognition in videos a survey"):
  with members:
    - "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"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-4 (induced by source paper "learning visual actions using multiple verb only labels"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "end to end dense video captioning with masked transformer"
    - "reconstruction network for video captioning"
    - "what actions are needed for understanding human actions in videos"
    - "video captioning via hierarchical reinforcement learning"
    - "convolutional image captioning"
    - "zero shot activity recognition with verb attribute induction"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-5 (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"
    - "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"
    - "generalized zero shot learning for action recognition with web scale video data"

Connected through hyperedge HE-6 (induced by source paper "zero shot recognition of complex action sequences"):
  with members:
    - "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"
    - "temporal relational reasoning in videos"

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)