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: "the pros and cons rank aware temporal attention for skill determination in long videos"
  Abstract: We present a new model to determine relative skill from long videos, through learnable temporal attention modules. Skill determination is formulated as a ranking problem, making it suitable for common and generic tasks. However, for long videos, parts of the video are irrelevant for assessing skill, and there may be variability in the skill exhibited throughout a video. We therefore propose a method which assesses the relative overall level of skill in a long video by attending to its skill-relevant parts. Our approach trains temporal attention modules, learned with only video-level supervision, using a novel rank-aware loss function. In addition to attending to task relevant video parts, our proposed loss jointly trains two attention modules to separately attend to video parts which are indicative of higher (pros) and lower (cons) skill. We evaluate our approach on the EPIC-Skills dataset and additionally annotate a larger dataset from YouTube videos for skill determination with five previously unexplored tasks. Our method outperforms previous approaches and classic softmax attention on both datasets by over 4% pairwise accuracy, and as much as 12% on individual tasks. We also demonstrate our model's ability to attend to rank-aware parts of the 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"
    - "evaluating surgical skills from kinematic data using convolutional neural networks"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "who s better who s best pairwise deep ranking for skill determination"
    - "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 "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-3 (induced by source paper "hallucinet ing spatiotemporal representations using 2d cnn"):
  with members:
    - "design light weight 3d convolutional networks for video recognition temporal residual fully separable block and fast algorithm"
    - "what and how well you performed a multitask learning approach to action quality assessment"
    - "efficient video classification using fewer frames"
    - "temporal sequence distillation towards few frame action recognition in videos"
    - "motion feature network fixed motion filter for action recognition"
    - "rgb d based action recognition with light weight 3d convolutional networks"
    - "slowfast networks for video recognition"
    - "the activitynet large scale activity recognition challenge 2018 summary"

Connected through hyperedge HE-4 (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"
    - "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"
    - "who s better who s best pairwise deep ranking for skill determination"
    - "extraction and classification of diving clips from continuous video footage"

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)