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: "pooled motion features for first person videos"
  Abstract: In this paper, we present a new feature representation for first-person videos. In first-person video understanding (e.g., activity recognition), it is very important to capture both entire scene dynamics (i.e., egomotion) and salient local motion observed in videos. We describe a representation framework based on time series pooling, which is designed to abstract short-term/long-term changes in feature descriptor elements. The idea is to keep track of how descriptor values are changing over time and summarize them to represent motion in the activity video. The framework is general, handling any types of per-frame feature descriptors including conventional motion descriptors like histogram of optical flows (HOF) as well as appearance descriptors from more recent convolutional neural networks (CNN). We experimentally confirm that our approach clearly outperforms previous feature representations including bag-of-visual-words and improved Fisher vector (IFV) when using identical underlying feature descriptors. We also confirm that our feature representation has superior performance to existing state-of-the-art features like local spatio-temporal features and Improved Trajectory Features (originally developed for 3rd-person videos) when handling first-person videos. Multiple first-person activity datasets were tested under various settings to confirm these findings.

Connected through hyperedge HE-1 (induced by source paper "three stream fusion network for first person interaction recognition"):
  with members:
    - "robust and discriminative labeling for multi label active learning based on maximum correntropy criterion"
    - "spatiotemporal pyramid network for video action recognition"
    - "non linear temporal subspace representations for activity recognition"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "boosted multiple kernel learning for first person activity recognition"
    - "attentional pooling for action recognition"
    - "convolutional long short term memory networks for recognizing first person interactions"
    - "action recognition with coarse to fine deep feature integration and asynchronous fusion"

Connected through hyperedge HE-2 (induced by source paper "view invariant deep architecture for human action recognition using two stream motion and shape temporal dynamics"):
  with members:
    - "action recognition for depth video using multi view dynamic images"
    - "compressed video action recognition"
    - "temporal 3d convnets new architecture and transfer learning for video classification"
    - "learning human pose models from synthesized data for robust rgb d action recognition"
    - "skepxels spatio temporal image representation of human skeleton joints for action recognition"
    - "action recognition with coarse to fine deep feature integration and asynchronous fusion"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "learning a deep model for human action recognition from novel viewpoints"

Connected through hyperedge HE-3 (induced by source paper "joint 2d 3d breast cancer classification"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "end to end training for whole image breast cancer diagnosis using an all convolutional design"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "rank pooling for action recognition"
    - "detecting and classifying lesions in mammograms with deep learning"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"):
  with members:
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "when will you do what anticipating temporal occurrences of activities"
    - "lsta long short term attention for egocentric action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "forecasting hand and object locations in future frames"
    - "am i done predicting action progress in videos"
    - "encouraging lstms to anticipate actions very early"

Connected through hyperedge HE-7 (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-8 (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"

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)