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: "vlase vehicle localization by aggregating semantic edges"
  Abstract: In this paper, we propose VLASE, a framework to use semantic edge features from images to achieve on-road localization. Semantic edge features denote edge contours that separate pairs of distinct objects such as building-sky, road- sidewalk, and building-ground. While prior work has shown promising results by utilizing the boundary between prominent classes such as sky and building using skylines, we generalize this approach to consider semantic edge features that arise from 19 different classes. Our localization algorithm is simple, yet very powerful. We extract semantic edge features using a recently introduced CASENet architecture and utilize VLAD framework to perform image retrieval. Our experiments show that we achieve improvement over some of the state-of-the-art localization algorithms such as SIFT-VLAD and its deep variant NetVLAD. We use ablation study to study the importance of different semantic classes and show that our unified approach achieves better performance compared to individual prominent features such as skylines.

Connected through hyperedge HE-1 (induced by source paper "fine grained segmentation networks self supervised segmentation for improved long term visual localization"):
  with members:
    - "sparse to dense hypercolumn matching for long term visual localization"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "a cross season correspondence dataset for robust semantic segmentation"
    - "deep clustering for unsupervised learning of visual features"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-2 (induced by source paper "a cross season correspondence dataset for robust semantic segmentation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "long term visual localization using semantically segmented images"
    - "image to image translation for domain adaptation"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "cycada cycle consistent adversarial domain adaptation"
    - "road reality oriented adaptation for semantic segmentation of urban scenes"
    - "semantic visual localization"

Connected through hyperedge HE-3 (induced by source paper "is this the right place geometric semantic pose verification for indoor visual localization"):
  with members:
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "taskonomy disentangling task transfer learning"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "long term visual localization using semantically segmented images"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "geometric loss functions for camera pose regression with deep learning"
    - "benchmarking 6dof urban visual localization in changing conditions"

Connected through hyperedge HE-4 (induced by source paper "devil is in the edges learning semantic boundaries from noisy annotations"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "simultaneous edge alignment and learning"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "deep active contours"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deep watershed transform for instance segmentation"
    - "image to image translation with conditional adversarial networks"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-5 (induced by source paper "image based place recognition on bucolic environment across seasons from semantic edge description"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "x view graph based semantic multi view localization"
    - "casenet deep category aware semantic edge detection"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "large scale image retrieval with attentive deep local features"
    - "pyramid scene parsing network"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "neural codes for image retrieval"

Connected through hyperedge HE-6 (induced by source paper "ideal cities a trustworthy and sustainable framework for circular smart cities"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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)