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: "sembed semantic embedding of egocentric action videos"
  Abstract: We present SEMBED, an approach for embedding an egocentric object interaction video in a semantic-visual graph to estimate the probability distribution over its potential semantic labels. When object interactions are annotated using unbounded choice of verbs, we embrace the wealth and ambiguity of these labels by capturing the semantic relationships as well as the visual similarities over motion and appearance features. We show how SEMBED can interpret a challenging dataset of 1225 freely annotated egocentric videos, outperforming SVM classification by more than 5%.

Connected through hyperedge HE-1 (induced by source paper "multitask learning to improve egocentric action recognition"):
  with members:
    - "mutual context network for jointly estimating egocentric gaze and actions"
    - "saliency tubes visual explanations for spatio temporal convolutions"
    - "egocentric hand track and object based human action recognition"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "long term feature banks for detailed video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "multi fiber networks for video recognition"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "learning visual actions using multiple verb only labels"):
  with members:
    - "action2vec a crossmodal embedding approach to action learning"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "towards an unequivocal representation of actions"):
  with members:
    - "what actions are needed for understanding human actions in videos"
    - "improving classification by improving labelling introducing probabilistic multi label object interaction recognition"
    - "the kinetics human action video dataset"
    - "hcp a flexible cnn framework for multi label image classification"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "unsupervised visual sense disambiguation for verbs using multimodal embeddings"
    - "convolutional two stream network fusion for video action recognition"
    - "describing common human visual actions in images"

Connected through hyperedge HE-4 (induced by source paper "first person hand action benchmark with rgb d videos and 3d hand pose annotations"):
  with members:
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "the 2017 hands in the million challenge on 3d hand pose estimation"
    - "real time hand tracking under occlusion from an egocentric rgb d sensor"
    - "depth based 3d hand pose estimation from current achievements to future goals"
    - "convolutional pose machines"
    - "training a feedback loop for hand pose estimation"

Connected through hyperedge HE-5 (induced by source paper "improving classification by improving labelling introducing probabilistic multi label object interaction recognition"):
  with members:
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "multi label image classification with regional latent semantic dependencies"
    - "going deeper into first person activity recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "compact cnn for indexing egocentric videos"
    - "language models for image captioning the quirks and what works"
    - "unsupervised learning from narrated instruction videos"
    - "deep compositional captioning describing novel object categories without paired training data"

Connected through hyperedge HE-6 (induced by source paper "convolutional long short term memory networks for recognizing first person interactions"):
  with members:
    - "boosted multiple kernel learning for first person activity recognition"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "anomaly detection in video using predictive convolutional long short term memory networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "compact cnn for indexing egocentric videos"
    - "spatio temporal video autoencoder with differentiable memory"
    - "unsupervised learning of video representations using lstms"
    - "human action recognition using factorized spatio temporal convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "trespassing the boundaries labeling temporal bounds for object interactions in egocentric video"):
  with members:
    - "segmental spatio temporal cnns for fine grained action segmentation and classification"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "connectionist temporal modeling for weakly supervised action labeling"
    - "going deeper into first person activity recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "pooled motion features for first person videos"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

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)