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: "what players do with the ball a physically constrained interaction modeling"
  Abstract: Tracking the ball is critical for video-based analysis of team sports. However, it is difficult, especially in low-resolution images, due to the small size of the ball, its speed that creates motion blur, and its often being occluded by players. In this paper, we propose a generic and principled approach to modeling the interaction between the ball and the players while also imposing appropriate physical constraints on the ball's trajectory. We show that our approach, formulated in terms of a Mixed Integer Program, is more robust and more accurate than several state-of-the-art approaches on real-life volleyball, basketball, and soccer sequences.

Connected through hyperedge HE-1 (induced by source paper "a light dual task neural network for haze removal"):
  with members:
    - "gated fusion network for single image dehazing"
    - "fisheyerecnet a multi context collaborative deep network for fisheye image rectification"
    - "improving object detection from scratch via gated feature reuse"
    - "deep voice real time neural text to speech"
    - "an all in one network for dehazing and beyond"
    - "tensorlayer a versatile library for efficient deep learning development"
    - "dehazenet an end to end system for single image haze removal"
    - "predicting people s 3d poses from short sequences"

Connected through hyperedge HE-2 (induced by source paper "patch alignment manifold matting"):
  with members:
    - "sparse coding for alpha matting"
    - "principal manifolds and nonlinear dimension reduction via local tangent space alignment"

Connected through hyperedge HE-3 (induced by source paper "a causal and or graph model for visibility fluent reasoning in tracking interacting objects"):
  with members:
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "convolutional pose machines"
    - "recognizing car fluents from video"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "a parameterized approach to personalized variable length summarization of soccer matches"):
  with members:
    - "leveraging contextual cues for generating basketball highlights"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "a causal and or graph model for visibility fluent reasoning in human object interactions"):
  with members:
    - "convolutional pose machines"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "am i a baller basketball performance assessment from first person videos"):
  with members:
    - "first person action object detection with egonet"
    - "going deeper into first person activity recognition"
    - "attention to scale scale aware semantic image segmentation"
    - "a hierarchical deep temporal model for group activity recognition"
    - "detecting events and key actors in multi person videos"
    - "predicting important objects for egocentric video summarization"
    - "flowing convnets for human pose estimation in videos"
    - "long term recurrent convolutional networks for visual recognition and description"

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)