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: "self localization from images with small overlap"
  Abstract: With the recent success of visual features from deep convolutional neural networks (DCNN) in visual robot self-localization, it has become important and practical to address more general self-localization scenarios. In this paper, we address the scenario of self-localization from images with small overlap. We explicitly introduce a localization difficulty index as a decreasing function of view overlap between query and relevant database images and investigate performance versus difficulty for challenging cross-view self-localization tasks. We then reformulate the self-localization as a scalable bag-of-visual-features (BoVF) scene retrieval and present an efficient solution called PCA-NBNN, aiming to facilitate fast and yet discriminative correspondence between partially overlapping images. The proposed approach adopts recent findings in discriminativity preserving encoding of DCNN features using principal component analysis (PCA) and cross-domain scene matching using naive Bayes nearest neighbor distance metric (NBNN). We experimentally demonstrate that the proposed PCA-NBNN framework frequently achieves comparable results to previous DCNN features and that the BoVF model is significantly more efficient. We further address an important alternative scenario of "self-localization from images with NO overlap" and report the result.

Connected through hyperedge HE-1 (induced by source paper "fault diagnosing slam for varying scale change detection"):
  with members:
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "multi attention recurrent network for human communication comprehension"
    - "map management for efficient long term visual localization in outdoor environments"
    - "learning long range perception using self supervision from short range sensors and odometry"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "detection by localization maintenance free change object detector"
    - "lightweight unsupervised deep loop closure"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-2 (induced by source paper "simultaneous detection of loop closures and changed objects"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "detection by localization maintenance free change object detector"
    - "lightweight unsupervised deep loop closure"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-3 (induced by source paper "detection by localization maintenance free change object detector"):
  with members:
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "yolov3 an incremental improvement"
    - "map management for efficient long term visual localization in outdoor environments"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "lightweight unsupervised deep loop closure"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "learning deep structured network for weakly supervised change detection"

Connected through hyperedge HE-4 (induced by source paper "learning deep nbnn representations for robust place categorization"):
  with members:
    - "places an image database for deep scene understanding"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on the performance of convnet features for place recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "locally scale invariant convolutional neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "long term ensemble learning of visual place classifiers"):
  with members:
    - "learning deep nbnn representations for robust place categorization"
    - "scalable greedy algorithms for transfer learning"
    - "random forests versus neural networks what s best for camera localization"
    - "active transfer learning with zero shot priors reusing past datasets for future tasks"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

Connected through hyperedge HE-6 (induced by source paper "change detection under global viewpoint uncertainty"):
  with members:
    - "bags of local convolutional features for scalable instance search"
    - "sift meets cnn a decade survey of instance retrieval"
    - "particular object retrieval with integral max pooling of cnn activations"

Connected through hyperedge HE-7 (induced by source paper "multi model hypothesize and verify approach for incremental loop closure verification"):
  with members:
    - "incremental ransac for online relocation in large dynamic environments"
    - "neural codes for image retrieval"

Connected through hyperedge HE-8 (induced by source paper "deformable map matching for uncertain loop less maps"):
  with members:
    - "neural codes for image retrieval"

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)