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: "frequency domain transformer networks for video prediction"
  Abstract: The task of video prediction is forecasting the next frames given some previous frames. Despite much recent progress, this task is still challenging mainly due to high nonlinearity in the spatial domain. To address this issue, we propose a novel architecture, Frequency Domain Transformer Network (FDTN), which is an end-to-end learnable model that estimates and uses the transformations of the signal in the frequency domain. Experimental evaluations show that this approach can outperform some widely used video prediction methods like Video Ladder Network (VLN) and Predictive Gated Pyramids (PGP).

Connected through hyperedge HE-1 (induced by source paper "pisep 2 pseudo image sequence evolution based 3d pose prediction"):
  with members:
    - "human action recognition and prediction a survey"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "deep video generation prediction and completion of human action sequences"
    - "stochastic adversarial video prediction"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "timeception for complex action recognition"
    - "long term human motion prediction by modeling motion context and enhancing motion dynamic"
    - "learning to decompose and disentangle representations for video prediction"

Connected through hyperedge HE-2 (induced by source paper "utilizing temporal information in deep convolutional network for efficient soccer ball detection and tracking"):
  with members:
    - "playing soccer without colors in the spl a convolutional neural network approach"
    - "deep learning for semantic segmentation on minimal hardware"
    - "a monocular vision system for playing soccer in low color information environments"
    - "nimbro op2x adult sized open source 3d printed humanoid robot"
    - "visual mesh real time object detection using constant sample density"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "temporal convolutional networks for action segmentation and detection"

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)