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: "transfer learning through greedy subset selection"
  Abstract: In this work we study the binary transfer learning problem involving $10^{2}$-$10^{3}$ sources. We focus on how to select sources from the large pool and how to combine them to yield a good performance on a target task. In particular, we consider the transfer learning setting where one does not have direct access to the source data, but rather employs the source hypotheses trained from them. Building on results on greedy algorithms, we propose an efficient algorithm that selects relevant source hypotheses and feature dimensions simultaneously. On three computer vision datasets we achieve state-of-the-art results, substantially outperforming both popular feature selection and transfer learning baselines when transferring in a small-sample setting. Our experiments involve up to $1000$ classes, totalling $1.2$ million examples, with only $11$ to $20$ training examples from the target domain. We corroborate our findings showing theoretically that, under reasonable assumptions on the source hypotheses, our algorithm can learn effectively from few examples.

Connected through hyperedge HE-1 (induced by source paper "combining federated and active learning for communication efficient distributed failure prediction in aeronautics"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "differentially private federated learning a client level perspective"
    - "communication efficient learning of deep networks from decentralized data"
    - "active learning with statistical models"

Connected through hyperedge HE-2 (induced by source paper "scalable greedy algorithms for transfer learning"):
  with members:
    - "when naive bayes nearest neighbours meet convolutional neural networks"
    - "learning transferable features with deep adaptation networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "associative embeddings for large scale knowledge transfer with self assessment"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "fast rates by transferring from auxiliary hypotheses"):
  with members:
    - "one shot adaptation of supervised deep convolutional models"
    - "optimistic rates for learning with a smooth loss"
    - "regularization techniques for learning with matrices"
    - "frustratingly easy domain adaptation"
    - "domain adaptation learning bounds and algorithms"
    - "learning to learn"

Connected through hyperedge HE-4 (induced by source paper "optimal transport for deep joint transfer learning"):
  with members:
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "unsupervised domain adaptation with residual transfer networks"
    - "learning transferable features with deep adaptation networks"
    - "learning with a wasserstein loss"

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)