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: "who s better who s best pairwise deep ranking for skill determination"
  Abstract: We present a method for assessing skill from video, applicable to a variety of tasks, ranging from surgery to drawing and rolling pizza dough. We formulate the problem as pairwise (who's better?) and overall (who's best?) ranking of video collections, using supervised deep ranking. We propose a novel loss function that learns discriminative features when a pair of videos exhibit variance in skill, and learns shared features when a pair of videos exhibit comparable skill levels. Results demonstrate our method is applicable across tasks, with the percentage of correctly ordered pairs of videos ranging from 70% to 83% for four datasets. We demonstrate the robustness of our approach via sensitivity analysis of its parameters. We see this work as effort toward the automated organization of how-to video collections and overall, generic skill determination in video.

Connected through hyperedge HE-1 (induced by source paper "action quality assessment using siamese network based deep metric learning"):
  with members:
    - "what and how well you performed a multitask learning approach to action quality assessment"
    - "3d human action recognition with siamese lstm based deep metric learning"
    - "the pros and cons rank aware temporal attention for skill determination in long videos"
    - "evaluating surgical skills from kinematic data using convolutional neural networks"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "stacked hourglass networks for human pose estimation"
    - "learning to score olympic events"
    - "measuring the quality of exercises"

Connected through hyperedge HE-2 (induced by source paper "toward adaptive guidance modeling the variety of user behaviors in continuous skill improving experiences of machine operation tasks"):
  with members:
    - "manipulation skill assessment from videos with spatial attention network"
    - "what and how well you performed a multitask learning approach to action quality assessment"
    - "automated capture and delivery of assistive task guidance with an eyewear computer the glaciar system"

Connected through hyperedge HE-3 (induced by source paper "action modifiers learning from adverbs in instructional videos"):
  with members:
    - "manipulation skill assessment from videos with spatial attention network"
    - "weakly supervised video moment retrieval from text queries"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "wman weakly supervised moment alignment network for text based video segment retrieval"
    - "human action adverb recognition adha dataset and a three stream hybrid model"
    - "adversarial soft detection based aggregation network for image retrieval"
    - "motion appearance co memory networks for video question answering"
    - "attributes as operators factorizing unseen attribute object compositions"

Connected through hyperedge HE-4 (induced by source paper "manipulation skill assessment from videos with spatial attention network"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mutual context network for jointly estimating egocentric gaze and actions"
    - "boosted attention leveraging human attention for image captioning"
    - "bam bottleneck attention module"
    - "learning to score the figure skating sports videos"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-5 (induced by source paper "what and how well you performed a multitask learning approach to action quality assessment"):
  with members:
    - "manipulation skill assessment from videos with spatial attention network"
    - "learning to score the figure skating sports videos"
    - "the pros and cons rank aware temporal attention for skill determination in long videos"
    - "evaluating surgical skills from kinematic data using convolutional neural networks"
    - "video and accelerometer based motion analysis for automated surgical skills assessment"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "automated surgical skill assessment in rmis training"
    - "extraction and classification of diving clips from continuous video footage"

Connected through hyperedge HE-6 (induced by source paper "video based surgical skill assessment using 3d convolutional neural networks"):
  with members:
    - "deep learning with convolutional neural network for objective skill evaluation in robot assisted surgery"
    - "evaluating surgical skills from kinematic data using convolutional neural networks"
    - "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "video and accelerometer based motion analysis for automated surgical skills assessment"
    - "temporal segment networks for action recognition in videos"
    - "automated surgical skill assessment in rmis training"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-7 (induced by source paper "the pros and cons rank aware temporal attention for skill determination in long videos"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning to score the figure skating sports videos"
    - "w talc weakly supervised temporal activity localization and classification"
    - "deep learning with convolutional neural network for objective skill evaluation in robot assisted surgery"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-8 (induced by source paper "scaling egocentric vision the epic kitchens dataset"):
  with members:
    - "action completion a temporal model for moment detection"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "from lifestyle vlogs to everyday interactions"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "hacs human action clips and segments dataset for recognition and temporal localization"
    - "towards automatic learning of procedures from web instructional videos"
    - "trespassing the boundaries labeling temporal bounds for object interactions in egocentric video"
    - "the something something video database for learning and evaluating visual common sense"

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)