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: "am i a baller basketball performance assessment from first person videos"
  Abstract: This paper presents a method to assess a basketball player's performance from his/her first-person video. A key challenge lies in the fact that the evaluation metric is highly subjective and specific to a particular evaluator. We leverage the first-person camera to address this challenge. The spatiotemporal visual semantics provided by a first-person view allows us to reason about the camera wearer's actions while he/she is participating in an unscripted basketball game. Our method takes a player's first-person video and provides a player's performance measure that is specific to an evaluator's preference. #R##N#To achieve this goal, we first use a convolutional LSTM network to detect atomic basketball events from first-person videos. Our network's ability to zoom-in to the salient regions addresses the issue of a severe camera wearer's head movement in first-person videos. The detected atomic events are then passed through the Gaussian mixtures to construct a highly non-linear visual spatiotemporal basketball assessment feature. Finally, we use this feature to learn a basketball assessment model from pairs of labeled first-person basketball videos, for which a basketball expert indicates, which of the two players is better. #R##N#We demonstrate that despite not knowing the basketball evaluator's criterion, our model learns to accurately assess the players in real-world games. Furthermore, our model can also discover basketball events that contribute positively and negatively to a player's performance.

Connected through hyperedge HE-1 (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-2 (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"
    - "who s better who s best pairwise deep ranking for skill determination"

Connected through hyperedge HE-3 (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-4 (induced by source paper "action quality assessment across multiple actions"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "who s better who s best pairwise deep ranking for skill determination"
    - "learning to score olympic events"
    - "measuring the quality of exercises"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "towards structured analysis of broadcast badminton videos"):
  with members:
    - "segmental spatiotemporal cnns for fine grained action segmentation"
    - "wavenet a generative model for raw audio"
    - "temporal convolutional networks a unified approach to action segmentation"
    - "learning to track at 100 fps with deep regression networks"
    - "trajectory aligned features for first person action recognition"
    - "deep residual learning for image recognition"
    - "a hierarchical deep temporal model for group activity recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-6 (induced by source paper "who s better who s best pairwise deep ranking for skill determination"):
  with members:
    - "joint discovery of object states and manipulation actions"
    - "video and accelerometer based motion analysis for automated surgical skills assessment"
    - "evaluationnet can human skill be evaluated by deep networks"
    - "automated surgical skill assessment in rmis training"
    - "learning to score olympic events"
    - "temporal segment networks towards good practices for deep action recognition"
    - "top down neural attention by excitation backprop"
    - "unsupervised learning from narrated instruction 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)