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: "casualty detection from 3d point cloud data for autonomous ground mobile rescue robots"
  Abstract: One of the most important features of mobile rescue robots is the ability to autonomously detect casualties, i.e. human bodies, which are usually lying on the ground. This paper proposes a novel method for autonomously detecting casualties lying on the ground using obtained 3D point-cloud data from an on-board sensor, such as an RGB-D camera or a 3D LIDAR, on a mobile rescue robot. In this method, the obtained 3D point-cloud data is projected onto the detected ground plane, i.e. floor, within the point cloud. Then, this projected point cloud is converted into a grid-map that is used afterwards as an input for the algorithm to detect human body shapes. The proposed method is evaluated by performing detections of a human dummy, placed in different random positions and orientations, using an on-board RGB-D camera on a mobile rescue robot called ResQbot. To evaluate the robustness of the casualty detection method to different camera angles, the orientation of the camera is set to different angles. The experimental results show that using the point-cloud data from the on-board RGB-D camera, the proposed method successfully detects the casualty in all tested body positions and orientations relative to the on-board camera, as well as in all tested camera angles.

Connected through hyperedge HE-1 (induced by source paper "sim to real learning for casualty detection from ground projected point cloud data"):
  with members:
    - "resqbot a mobile rescue robot with immersive teleperception for casualty extraction"
    - "focal loss for dense object detection"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "megdet a large mini batch object detector"
    - "learning from synthetic humans"
    - "mask r cnn"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "synthesizing training images for boosting human 3d pose estimation"

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.RO(Robotics)