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: "whoi plankton a large scale fine grained visual recognition benchmark dataset for plankton classification"
  Abstract: Planktonic organisms are of fundamental importance to marine ecosystems: they form the basis of the food web, provide the link between the atmosphere and the deep ocean, and influence global-scale biogeochemical cycles. Scientists are increasingly using imaging-based technologies to study these creatures in their natural habit. Images from such systems provide an unique opportunity to model and understand plankton ecosystems, but the collected datasets can be enormous. The Imaging FlowCytobot (IFCB) at Woods Hole Oceanographic Institution, for example, is an \emph{in situ} system that has been continuously imaging plankton since 2006. To date, it has generated more than 700 million samples. Manual classification of such a vast image collection is impractical due to the size of the data set. In addition, the annotation task is challenging due to the large space of relevant classes, intra-class variability, and inter-class similarity. Methods for automated classification exist, but the accuracy is often below that of human experts. Here we introduce WHOI-Plankton: a large scale, fine-grained visual recognition dataset for plankton classification, which comprises over 3.4 million expert-labeled images across 70 classes. The labeled image set is complied from over 8 years of near continuous data collection with the IFCB at the Martha's Vineyard Coastal Observatory (MVCO). We discuss relevant metrics for evaluation of classification performance and provide results for a traditional method based on hand-engineered features and two methods based on convolutional neural networks.

Connected through hyperedge HE-1 (induced by source paper "few shot learning for domain specfic fine grained image classfication"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "few shot learning with metric agnostic conditional embeddings"
    - "discriminative learning of latent features for zero shot recognition"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "multi scale location aware kernel representation for object detection"
    - "adversarial complementary learning for weakly supervised object localization"
    - "git loss for deep face recognition"
    - "dynamic few shot visual learning without forgetting"

Connected through hyperedge HE-2 (induced by source paper "beyond image classification zooplankton identification with deep vector space embeddings"):
  with members:
    - "tensorflow a system for large scale machine learning"
    - "facenet a unified embedding for face recognition and clustering"
    - "rethinking the inception architecture for computer vision"
    - "learning fine grained image similarity with deep ranking"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-3 (induced by source paper "quantification in the wild data sets and baselines"):
  with members:
    - "simultaneous deep transfer across domains and tasks"
    - "random sampling in an age of automation minimizing expenditures through balanced collection and annotation"
    - "cost effective sampling for pairs of annotators"
    - "caffe convolutional architecture for fast feature embedding"
    - "domain adaptation for statistical classifiers"

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)