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: "unsupervised learning of important objects from first person videos"
  Abstract: A first-person camera, placed at a person's head, captures, which objects are important to the camera wearer. Most prior methods for this task learn to detect such important objects from the manually labeled first-person data in a supervised fashion. However, important objects are strongly related to the camera wearer's internal state such as his intentions and attention, and thus, only the person wearing the camera can provide the importance labels. Such a constraint makes the annotation process costly and limited in scalability. #R##N#In this work, we show that we can detect important objects in first-person images without the supervision by the camera wearer or even third-person labelers. We formulate an important detection problem as an interplay between the 1) segmentation and 2) recognition agents. The segmentation agent first proposes a possible important object segmentation mask for each image, and then feeds it to the recognition agent, which learns to predict an important object mask using visual semantics and spatial features. #R##N#We implement such an interplay between both agents via an alternating cross-pathway supervision scheme inside our proposed Visual-Spatial Network (VSN). Our VSN consists of spatial ("where") and visual ("what") pathways, one of which learns common visual semantics while the other focuses on the spatial location cues. Our unsupervised learning is accomplished via a cross-pathway supervision, where one pathway feeds its predictions to a segmentation agent, which proposes a candidate important object segmentation mask that is then used by the other pathway as a supervisory signal. We show our method's success on two different important object datasets, where our method achieves similar or better results as the supervised methods.

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 "hand priming in object localization for assistive egocentric vision"):
  with members:
    - "digging deeper into egocentric gaze prediction"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "scaling egocentric vision the epic kitchens dataset"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "next active object prediction from egocentric videos"
    - "mask r cnn"
    - "fully convolutional instance aware semantic segmentation"

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 "probabilistic atlases to enforce topological constraints"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "voxelatlasgan 3d left ventricle segmentation on echocardiography with atlas guided generation and voxel to voxel discrimination"
    - "enhancing label driven deep deformable image registration with local distance metrics for state of the art cardiac motion tracking"
    - "spatially localized atlas network tiles enables 3d whole brain segmentation from limited data"
    - "efficient video object segmentation via network modulation"
    - "improving cytoarchitectonic segmentation of human brain areas with self supervised siamese networks"
    - "parcellation of visual cortex on high resolution histological brain sections using convolutional neural networks"
    - "residual attention network for image classification"

Connected through hyperedge HE-5 (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"
    - "first person action object detection with egonet"
    - "benchmarking state of the art deep learning software tools"
    - "guesswhat visual object discovery through multi modal dialogue"

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)