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: "3d multirater rcnn for multimodal multiclass detection and characterisation of extremely small objects"
  Abstract: Extremely small objects (ESO) have become observable on clinical routine magnetic resonance imaging acquisitions, thanks to a reduction in acquisition time at higher resolution. Despite their small size (usually $<$10 voxels per object for an image of more than $10^6$ voxels), these markers reflect tissue damage and need to be accounted for to investigate the complete phenotype of complex pathological pathways. In addition to their very small size, variability in shape and appearance leads to high labelling variability across human raters, resulting in a very noisy gold standard. Such objects are notably present in the context of cerebral small vessel disease where enlarged perivascular spaces and lacunes, commonly observed in the ageing population, are thought to be associated with acceleration of cognitive decline and risk of dementia onset. In this work, we redesign the RCNN model to scale to 3D data, and to jointly detect and characterise these important markers of age-related neurovascular changes. We also propose training strategies enforcing the detection of extremely small objects, ensuring a tractable and stable training process.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised object detection with 2d and 3d regression neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self paced deep learning for weakly supervised object detection"
    - "visual interpretability for deep learning a survey"
    - "attention based deep multiple instance learning"
    - "model agnostic saliency for weakly supervised lesion detection from breast dce mri"
    - "deep learning from label proportions for emphysema quantification"
    - "weakly supervised salient object detection using image labels"
    - "attention u net learning where to look for the pancreas"

Connected through hyperedge HE-2 (induced by source paper "needles in haystacks on classifying tiny objects in large images"):
  with members:
    - "pathologist level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "r 2 cnn fast tiny object detection in large scale remote sensing images"
    - "processing megapixel images with deep attention sampling models"
    - "attention based deep multiple instance learning"
    - "a robust and effective approach towards accurate metastasis detection and pn stage classification in breast cancer"
    - "exploring the limits of weakly supervised pretraining"
    - "three factors influencing minima in sgd"

Connected through hyperedge HE-3 (induced by source paper "let s agree to disagree learning highly debatable multirater labelling"):
  with members:
    - "learning from noisy labels by regularized estimation of annotator confusion"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "learning from noisy labels with distillation"

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)