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: "a comparison and evaluation of map construction algorithms using vehicle tracking data"
  Abstract: Map construction methods automatically produce and/or update street map datasets using vehicle tracking data. Enabled by the ubiquitous generation of geo-referenced tracking data, there has been a recent surge in map construction algorithms coming from different computer science domains. A cross-comparison of the various algorithms is still very rare, since (i) algorithms and constructed maps are generally not publicly available and (ii) there is no standard approach to assess the result quality, given the lack of benchmark data and quantitative evaluation methods. This work represents a first comprehensive attempt to benchmark such map construction algorithms. We provide an evaluation and comparison of seven algorithms using four datasets and four different evaluation measures. In addition to this comprehensive comparison, we make our datasets, source code of map construction algorithms and evaluation measures publicly available on http://mapconstruction.org. . This site has been established as a repository for map construction data and algorithms and we invite other researchers to contribute by uploading code and benchmark data supporting their contributions to map construction algorithms.

Connected through hyperedge HE-1 (induced by source paper "coltrane convolutional trajectory network for deep map inference"):
  with members:
    - "a comparative review of recent kinect based action recognition algorithms"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "road network fusion for incremental map updates"
    - "riobusdata outlier detection in bus routes of rio de janeiro"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "map a crowd sensing system for automatic map semantics identification"

Connected through hyperedge HE-2 (induced by source paper "roadtagger robust road attribute inference with graph neural networks"):
  with members:
    - "aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks"
    - "assigning a grade accurate measurement of road quality using satellite imagery"
    - "roadtracer automatic extraction of road networks from aerial images"
    - "gated graph sequence neural networks"
    - "tensorflow a system for large scale machine learning"
    - "gated feedback recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "inferring and improving street maps with data driven automation"):
  with members:
    - "assigning a grade accurate measurement of road quality using satellite imagery"
    - "machine assisted map editing"
    - "roadtracer automatic extraction of road networks from aerial images"
    - "aerial image geolocalization from recognition and matching of roads and intersections"

Connected through hyperedge HE-4 (induced by source paper "machine assisted map editing"):
  with members:
    - "roadtracer automatic extraction of road networks from aerial images"
    - "road network fusion for incremental map updates"
    - "iterative deep learning for network topology extraction"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "distance measures for embedded graphs"):
  with members:
    - "computing the gromov hausdorff distance for metric trees"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "subgraph isomorphism in planar graphs and related problems"

Connected through hyperedge HE-6 (induced by source paper "roadtracer automatic extraction of road networks from aerial images"):
  with members:
    - "kharita robust map inference using graph spanners"
    - "aerial image geolocalization from recognition and matching of roads and intersections"

Connected through hyperedge HE-7 (induced by source paper "road network fusion for incremental map updates"):
  with members:
    - "kharita robust map inference using graph spanners"

Connected through hyperedge HE-8 (induced by source paper "efficient generation of geographically accurate transit maps"):
  with members:
    - "ordering metro lines by block crossings"
    - "metro line crossing minimization hardness approximations and tractable cases"
    - "untangling a planar graph"
    - "a polynomial bound for untangling geometric planar graphs"

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.CG(Computational Geometry)