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: "mixedpeds pedestrian detection in unannotated videos using synthetically generated human agents for training"
  Abstract: We present a new method for training pedestrian detectors on an unannotated set of images. We produce a mixed reality dataset that is composed of real-world background images and synthetically generated static human-agents. Our approach is general, robust, and makes no other assumptions about the unannotated dataset regarding the number or location of pedestrians. We automatically extract from the dataset: i) the vanishing point to calibrate the virtual camera, and ii) the pedestrians' scales to generate a Spawn Probability Map, which is a novel concept that guides our algorithm to place the pedestrians at appropriate locations. After putting synthetic human-agents in the unannotated images, we use these augmented images to train a Pedestrian Detector, with the annotations generated along with the synthetic agents. We conducted our experiments using Faster R-CNN by comparing the detection results on the unannotated dataset performed by the detector trained using our approach and detectors trained with other manually labeled datasets. We showed that our approach improves the average precision by 5-13% over these detectors.

Connected through hyperedge HE-1 (induced by source paper "augmented lidar simulator for autonomous driving"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "meta sim learning to generate synthetic datasets"
    - "iou loss for 2d 3d object detection"
    - "mapless online detection of dynamic objects in 3d lidar"
    - "real time dynamic object detection for autonomous driving using prior 3d maps"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"

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"
    - "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"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

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"
    - "enabling robots to communicate their objectives"
    - "end to end learning for self driving cars"
    - "lcrowdv generating labeled videos for simulation based crowd behavior learning"

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)