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: "latch learned arrangements of three patch codes"
  Abstract: We present a novel means of describing local image appearances using binary strings. Binary descriptors have drawn increasing interest in recent years due to their speed and low memory footprint. A known shortcoming of these representations is their inferior performance compared to larger, histogram based descriptors such as the SIFT. Our goal is to close this performance gap while maintaining the benefits attributed to binary representations. To this end we propose the Learned Arrangements of Three Patch Codes descriptors, or LATCH. Our key observation is that existing binary descriptors are at an increased risk from noise and local appearance variations. This, as they compare the values of pixel pairs; changes to either of the pixels can easily lead to changes in descriptor values, hence damaging its performance. In order to provide more robustness, we instead propose a novel means of comparing pixel patches. This ostensibly small change, requires a substantial redesign of the descriptors themselves and how they are produced. Our resulting LATCH representation is rigorously compared to state-of-the-art binary descriptors and shown to provide far better performance for similar computation and space requirements.

Connected through hyperedge HE-1 (induced by source paper "features for ground texture based localization a survey"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "high precision localization using ground texture"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "lift learned invariant feature transform"

Connected through hyperedge HE-2 (induced by source paper "ground texture based localization using compact binary descriptors"):
  with members:
    - "features for ground texture based localization a survey"
    - "high precision localization using ground texture"

Connected through hyperedge HE-3 (induced by source paper "pan tilt zoom slam for sports videos"):
  with members:
    - "panoptic segmentation"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation"
    - "sports camera calibration via synthetic data"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "a two point method for ptz camera calibration in sports"
    - "soccer on your tabletop"
    - "light cascaded convolutional neural networks for accurate player detection"

Connected through hyperedge HE-4 (induced by source paper "collaborative localization for micro aerial vehicles"):
  with members:
    - "monocular vision based collaborative localization for micro aerial vehicle swarms"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

Connected through hyperedge HE-5 (induced by source paper "from handcrafted to deep local invariant features"):
  with members:
    - "local descriptors optimized for average precision"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "superpoint self supervised interest point detection and description"
    - "learning spread out local feature descriptors"
    - "lift learned invariant feature transform"
    - "densely connected convolutional networks"
    - "pn net conjoined triple deep network for learning local image descriptors"
    - "learning deep embeddings with histogram loss"

Connected through hyperedge HE-6 (induced by source paper "aggregating binary local descriptors for image retrieval"):
  with members:
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "exploiting local features from deep networks for image retrieval"
    - "a practical guide to cnns and fisher vectors for image instance retrieval"
    - "very deep convolutional networks for large scale image recognition"
    - "orientation covariant aggregation of local descriptors with embeddings"
    - "caffe convolutional architecture for fast feature embedding"
    - "neural codes for image retrieval"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-7 (induced by source paper "a two point method for ptz camera calibration in sports"):
  with members:
    - "light cascaded convolutional neural networks for accurate player detection"
    - "on the fly adaptation of regression forests for online camera relocalisation"
    - "backtracking regression forests for accurate camera relocalization"
    - "automated top view registration of broadcast football videos"
    - "continuous localization and mapping of a pan tilt zoom camera for wide area tracking"

Connected through hyperedge HE-8 (induced by source paper "vision based pose estimation for augmented reality a comparison study"):
  with members:
    - "lift learned invariant feature transform"
    - "the history of mobile augmented reality"
    - "mobile augmented reality survey a bottom up approach"
    - "faster and better a machine learning approach to corner detection"

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)