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 across networks for collective classification"
  Abstract: This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vector-based data, in which the instances are assumed to be independent and identically distributed, how to effectively transfer knowledge across different information networks has not been well studied, mainly because networks may have their distinct node features and link relationships between nodes. In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks. The proposed algorithm discovers these latent features by constructing label propagation matrices in the source and target networks, and mapping them into a shared latent feature space. The latent features capture common structure patterns shared by two networks, and serve as domain-independent features to be transferred between networks. Together with domain-dependent node features, we thereafter propose an iterative classification algorithm that leverages label correlations to predict node labels in the target network. Experiments on real-world networks demonstrate that our proposed algorithm can successfully achieve knowledge transfer between networks to help improve the accuracy of classifying nodes in the target network.

Connected through hyperedge HE-1 (induced by source paper "adversarial deep network embedding for cross network node classification"):
  with members:
    - "deep network embedding for graph representation learning in signed networks"
    - "adversarial training methods for network embedding"
    - "network together node classification via cross network deep network embedding"
    - "network transfer learning via adversarial domain adaptation with graph convolution"
    - "network embedding for cross network node classification"
    - "label efficient semi supervised learning via graph filtering"
    - "regal representation learning based graph alignment"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "network transfer learning via adversarial domain adaptation with graph convolution"):
  with members:
    - "deep network embedding for graph representation learning in signed networks"
    - "adversarial training methods for network embedding"
    - "beyond sharing weights for deep domain adaptation"
    - "attributed graph clustering via adaptive graph convolution"
    - "network embedding for cross network node classification"
    - "graph neural networks for social recommendation"
    - "label efficient semi supervised learning via graph filtering"
    - "regal representation learning based graph alignment"

Connected through hyperedge HE-3 (induced by source paper "network together node classification via cross network deep network embedding"):
  with members:
    - "deep network embedding for graph representation learning in signed networks"
    - "adversarial training methods for network embedding"
    - "regal representation learning based graph alignment"
    - "struc2vec learning node representations from structural identity"
    - "adversarial network embedding"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "don t walk skip online learning of multi scale network embeddings"

Connected through hyperedge HE-4 (induced by source paper "network embedding for cross network node classification"):
  with members:
    - "deep network embedding for graph representation learning in signed networks"
    - "struc2vec learning node representations from structural identity"
    - "semi supervised embedding in attributed networks with outliers"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-5 (induced by source paper "automatic difficulty management and testing in games using a framework based on behavior trees and genetic algorithms"):
  with members:
    - "learning of behavior trees for autonomous agents"
    - "faster algorithms for the constrained k means problem"

Connected through hyperedge HE-6 (induced by source paper "accelerating dependency graph learning from heterogeneous categorical event streams via knowledge transfer"):
  with members:
    - "modeling social networks with node attributes using the multiplicative attribute graph model"

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.LG(Machine Learning)