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: "compressed voxel based mapping using unsupervised learning"
  Abstract: In order to deal with the scaling problem of volumetric map representations, we propose spatially local methods for high-ratio compression of 3D maps, represented as truncated signed distance field ...

Connected through hyperedge HE-1 (induced by source paper "segmap segment based mapping and localization using data driven descriptors"):
  with members:
    - "segmap 3d segment mapping using data driven descriptors"
    - "learning a local feature descriptor for 3d lidar scans"
    - "seeing the wood for the trees reliable localization in urban and natural environments"
    - "in defense of the triplet loss for person re identification"
    - "semantic visual localization"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "segcloud semantic segmentation of 3d point clouds"
    - "segmatch segment based place recognition in 3d point clouds"

Connected through hyperedge HE-2 (induced by source paper "deepsdf learning continuous signed distance functions for shape representation"):
  with members:
    - "multi chart generative surface modeling"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "learning implicit fields for generative shape modeling"
    - "dynamic graph cnn for learning on point clouds"
    - "pcn point completion network"
    - "learning shape priors for single view 3d completion and reconstruction"
    - "geodesic convolutional shape optimization"
    - "occupancy networks learning 3d reconstruction in function space"

Connected through hyperedge HE-3 (induced by source paper "segmap 3d segment mapping using data driven descriptors"):
  with members:
    - "learning 3d segment descriptors for place recognition"
    - "segcloud semantic segmentation of 3d point clouds"
    - "segmatch segment based place recognition in 3d point clouds"
    - "vehicle detection from 3d lidar using fully convolutional network"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "vote3deep fast object detection in 3d point clouds using efficient convolutional neural networks"
    - "octnet learning deep 3d representations at high resolutions"
    - "shape completion enabled robotic grasping"

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)