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: "weakly supervised scene parsing with point based distance metric learning"
  Abstract: Semantic scene parsing is suffering from the fact that pixel-level annotations are hard to be collected. To tackle this issue, we propose a Point-based Distance Metric Learning (PDML) in this paper. PDML does not require dense annotated masks and only leverages several labeled points that are much easier to obtain to guide the training process. Concretely, we leverage semantic relationship among the annotated points by encouraging the feature representations of the intra- and inter-category points to keep consistent, i.e. points within the same category should have more similar feature representations compared to those from different categories. We formulate such a characteristic into a simple distance metric loss, which collaborates with the point-wise cross-entropy loss to optimize the deep neural networks. Furthermore, to fully exploit the limited annotations, distance metric learning is conducted across different training images instead of simply adopting an image-dependent manner. We conduct extensive experiments on two challenging scene parsing benchmarks of PASCAL-Context and ADE 20K to validate the effectiveness of our PDML, and competitive mIoU scores are achieved.

Connected through hyperedge HE-1 (induced by source paper "differential treatment for stuff and things a simple unsupervised domain adaptation method for semantic segmentation"):
  with members:
    - "alignseg feature aligned segmentation networks"
    - "domain adaptation for semantic segmentation with maximum squares loss"
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "constructing self motivated pyramid curriculums for cross domain semantic segmentation a non adversarial approach"
    - "panoptic deeplab a simple strong and fast baseline for bottom up panoptic segmentation"
    - "transfer adaptation learning a decade survey"

Connected through hyperedge HE-2 (induced by source paper "agriculture vision a large aerial image database for agricultural pattern analysis"):
  with members:
    - "foal fast online adaptive learning for cardiac motion estimation"
    - "higherhrnet scale aware representation learning for bottom up human pose estimation"
    - "differential treatment for stuff and things a simple unsupervised domain adaptation method for semantic segmentation"
    - "convolutional networks with dense connectivity"
    - "bottom up higher resolution networks for multi person pose estimation"
    - "isaid a large scale dataset for instance segmentation in aerial images"
    - "any precision deep neural networks"
    - "skynet a hardware efficient method for object detection and tracking on embedded systems"

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)