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: "spatio temporal analysis of team sports"
  Abstract: Team-based invasion sports such as football, basketball, and hockey are similar in the sense that the players are able to move freely around the playing area and that player and team performance cannot be fully analysed without considering the movements and interactions of all players as a group. State-of-the-art object tracking systems now produce spatio-temporal traces of player trajectories with high definition and high frequency, and this, in turn, has facilitated a variety of research efforts, across many disciplines, to extract insight from the trajectories. We survey recent research efforts that use spatio-temporal data from team sports as input and involve non-trivial computation. This article categorises the research efforts in a coherent framework and identifies a number of open research questions.

Connected through hyperedge HE-1 (induced by source paper "fsd 10 a dataset for competitive sports content analysis"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "what and how well you performed a multitask learning approach to action quality assessment"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "unsupervised learning via meta learning"
    - "a survey of content aware video analysis for sports"
    - "action quality assessment across multiple actions"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "temporal segment networks for action recognition in videos"

Connected through hyperedge HE-2 (induced by source paper "efficient nearest neighbor query and clustering of planar curves"):
  with members:
    - "locality sensitive hashing of curves"
    - "on the complexity of range searching among curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "computing the discrete fr echet distance in subquadratic time"

Connected through hyperedge HE-3 (induced by source paper "the vc dimension of metric balls under fr echet and hausdorff distances"):
  with members:
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "on the complexity of range searching among curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "a new upper bound for the vc dimension of visibility regions"

Connected through hyperedge HE-4 (induced by source paper "fresh fr echet similarity with hashing"):
  with members:
    - "approximate nearest neighbor search in high dimensions"
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "locality sensitive hashing of curves"
    - "on the complexity of range searching among curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast locality sensitive hashing frameworks for approximate near neighbor search"
    - "parameter free locality sensitive hashing for spherical range reporting"

Connected through hyperedge HE-5 (induced by source paper "learning person trajectory representations for team activity analysis"):
  with members:
    - "deep structured models for group activity recognition"
    - "human action recognition using factorized spatio temporal convolutional networks"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "a hierarchical deep temporal model for group activity recognition"
    - "fully connected deep structured networks"
    - "detecting events and key actors in multi person videos"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "automated top view registration of broadcast football videos"):
  with members:
    - "image to image translation with conditional adversarial networks"
    - "learning to see by moving"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "on the complexity of range searching among curves"):
  with members:
    - "locality sensitive hashing of curves"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"

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.OH(Other Computer Science)