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: "first person action object detection with egonet"
  Abstract: Unlike traditional third-person cameras mounted on robots, a first-person camera, captures a person's visual sensorimotor object interactions from up close. In this paper, we study the tight interplay between our momentary visual attention and motor action with objects from a first-person camera. We propose a concept of action-objects---the objects that capture person's conscious visual (watching a TV) or tactile (taking a cup) interactions. Action-objects may be task-dependent but since many tasks share common person-object spatial configurations, action-objects exhibit a characteristic 3D spatial distance and orientation with respect to the person. #R##N#We design a predictive model that detects action-objects using EgoNet, a joint two-stream network that holistically integrates visual appearance (RGB) and 3D spatial layout (depth and height) cues to predict per-pixel likelihood of action-objects. Our network also incorporates a first-person coordinate embedding, which is designed to learn a spatial distribution of the action-objects in the first-person data. We demonstrate EgoNet's predictive power, by showing that it consistently outperforms previous baseline approaches. Furthermore, EgoNet also exhibits a strong generalization ability, i.e., it predicts semantically meaningful objects in novel first-person datasets. Our method's ability to effectively detect action-objects could be used to improve robots' understanding of human-object interactions.

Connected through hyperedge HE-1 (induced by source paper "interaction graphs for object importance estimation in on road driving videos"):
  with members:
    - "goal oriented object importance estimation in on road driving videos"
    - "deeply supervised salient object detection with short connections"
    - "graph r cnn for scene graph generation"
    - "videos as space time region graphs"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "predicting driver attention in critical situations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-2 (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-3 (induced by source paper "a self validation network for object level human attention estimation"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "scaling egocentric vision the epic kitchens dataset"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "object level visual reasoning in videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "the kinetics human action video dataset"
    - "instance level salient object segmentation"

Connected through hyperedge HE-4 (induced by source paper "nemo future object localization using noisy ego priors"):
  with members:
    - "drogon a causal reasoning framework for future trajectory forecast"
    - "looking to relations for future trajectory forecast"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "convolutional neural network for trajectory prediction"
    - "joint person segmentation and identification in synchronized first and third person videos"

Connected through hyperedge HE-5 (induced by source paper "egocentric hand track and object based human action recognition"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "multi fiber networks for video recognition"
    - "yolov3 an incremental improvement"
    - "object level visual reasoning in videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "left right hand segmentation in egocentric videos"
    - "human action recognition pose based attention draws focus to hands"
    - "simple online and realtime tracking"

Connected through hyperedge HE-6 (induced by source paper "digging deeper into egocentric gaze prediction"):
  with members:
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "detecting engagement in egocentric video"
    - "two stream convolutional networks for dynamic saliency prediction"
    - "vanishing point detection with convolutional neural networks"
    - "recurrent mixture density network for spatiotemporal visual attention"
    - "generating videos with scene dynamics"
    - "the evolution of first person vision methods a survey"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "egocentric vision based future vehicle localization for intelligent driving assistance systems"):
  with members:
    - "goal oriented object importance estimation in on road driving videos"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "temporal recurrent networks for online action detection"
    - "joint person segmentation and identification in synchronized first and third person videos"
    - "active object perceiver recognition guided policy learning for object searching on mobile robots"

Connected through hyperedge HE-8 (induced by source paper "a survey on deep learning toolkits and libraries for intelligent user interfaces"):
  with members:
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "intelligent user interfaces a tutorial"
    - "machine teaching a new paradigm for building machine learning systems"
    - "rasa open source language understanding and dialogue management"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "benchmarking state of the art deep learning software tools"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "theano a python framework for fast computation of mathematical expressions"

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)