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: "lcrowdv generating labeled videos for simulation based crowd behavior learning"
  Abstract: We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework for generating crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior, flow, lighting conditions, viewpoint, noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by improving the accuracy of pedestrian detection and crowd behavior classification algorithms. LCrowdV would be released on the WWW.

Connected through hyperedge HE-1 (induced by source paper "automatically dataset augmentation using virtual human simulation"):
  with members:
    - "crowdnet a deep convolutional network for dense crowd counting"

Connected through hyperedge HE-2 (induced by source paper "pedestrian synthesis gan generating pedestrian data in real scene and beyond"):
  with members:
    - "pose guided person image generation"
    - "mixedpeds pedestrian detection in unannotated videos using synthetically generated human agents for training"
    - "unsupervised image to image translation networks"
    - "generative adversarial networks as variational training of energy based models"
    - "mmd gan towards deeper understanding of moment matching network"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "semi supervised learning with context conditional generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "identifying driver behaviors using trajectory features for vehicle navigation"):
  with members:
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"
    - "autonomous driving among many pedestrians models and algorithms"
    - "autonovi autonomous vehicle planning with dynamic maneuvers and traffic constraints"
    - "mixedpeds pedestrian detection in unannotated videos using synthetically generated human agents for training"
    - "enabling robots to communicate their objectives"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-4 (induced by source paper "mixedpeds pedestrian detection in unannotated videos using synthetically generated human agents for training"):
  with members:
    - "carla an open urban driving simulator"
    - "learning from synthetic humans"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "learning from simulated and unsupervised images through adversarial training"
    - "is faster r cnn doing well for pedestrian detection"
    - "detecting vanishing points using global image context in a non manhattan world"
    - "r fcn object detection via region based fully convolutional networks"
    - "fast r cnn"

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)