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: "compact cnn for indexing egocentric videos"
  Abstract: While egocentric video is becoming increasingly popular, browsing it is very difficult. In this paper we present a compact 3D Convolutional Neural Network (CNN) architecture for long-term activity recognition in egocentric videos. Recognizing long-term activities enables us to temporally segment (index) long and unstructured egocentric videos. Existing methods for this task are based on hand tuned features derived from visible objects, location of hands, as well as optical flow. Given a sparse optical flow volume as input, our CNN classifies the camera wearer's activity. We obtain classification accuracy of 89%, which outperforms the current state-of-the-art by 19%. Additional evaluation is performed on an extended egocentric video dataset, classifying twice the amount of categories than current state-of-the-art. Furthermore, our CNN is able to recognize whether a video is egocentric or not with 99.2% accuracy, up by 24% from current state-of-the-art. To better understand what the network actually learns, we propose a novel visualization of CNN kernels as flow fields.

Connected through hyperedge HE-1 (induced by source paper "h o unified egocentric recognition of 3d hand object poses and interactions"):
  with members:
    - "learning joint reconstruction of hands and manipulated objects"
    - "scaling egocentric vision the epic kitchens dataset"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "cross modal deep variational hand pose estimation"
    - "object level visual reasoning in videos"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "detecting and recognizing human object interactions"
    - "learning to estimate 3d hand pose from single rgb images"

Connected through hyperedge HE-2 (induced by source paper "forecasting human object interaction joint prediction of motor attention and egocentric activity"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "structured prediction helps 3d human motion modelling"
    - "paying more attention to motion attention distillation for learning video representations"
    - "deeply supervised salient object detection with short connections"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "binge watching scaling affordance learning from sitcoms"
    - "scaling egocentric vision the epic kitchens dataset"
    - "human action recognition and prediction a survey"

Connected through hyperedge HE-3 (induced by source paper "video skimming taxonomy and comprehensive survey"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "a general framework for edited video and raw video summarization"
    - "phd gifs personalized highlight detection for automatic gif creation"
    - "discovery of shared semantic spaces for multiscene video query and summarization"
    - "summarizing first person videos from third persons points of views"
    - "video summarization with attention based encoder decoder networks"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "collaborative summarization of topic related videos"

Connected through hyperedge HE-4 (induced by source paper "you2me inferring body pose in egocentric video via first and second person interactions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "simple baselines for human pose estimation and tracking"
    - "analyzing human human interactions a survey"
    - "detecting and recognizing human object interactions"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "learning feature pyramids for human pose estimation"
    - "detect and track efficient pose estimation in videos"

Connected through hyperedge HE-5 (induced by source paper "mutual context network for jointly estimating egocentric gaze and actions"):
  with members:
    - "visual dynamics stochastic future generation via layered cross convolutional networks"
    - "digging deeper into egocentric gaze prediction"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "on the role of event boundaries in egocentric activity recognition from photostreams"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "salgan visual saliency prediction with generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "making third person techniques recognize first person actions in egocentric videos"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "trajectory aligned features for first person action recognition"
    - "beyond short snippets deep networks for video classification"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "from third person to first person dataset and baselines for synthesis and retrieval"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "cross view image synthesis using geometry guided conditional gans"
    - "identifying first person camera wearers in third person videos"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "detecting engagement in egocentric video"
    - "egocentric meets top view"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-8 (induced by source paper "predicting visual context for unsupervised event segmentation in continuous photo streams"):
  with members:
    - "phd gifs personalized highlight detection for automatic gif creation"
    - "egocentric video description based on temporally linked sequences"
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "discovering picturesque highlights from egocentric vacation videos"
    - "unsupervised learning of video representations using lstms"
    - "predicting important objects for egocentric video summarization"

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)