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: "scalable graph learning for anti money laundering a first look"
  Abstract: Organized crime inflicts human suffering on a genocidal scale: the Mexican drug cartels have murdered 150,000 people since 2006, upwards of 700,000 people per year are "exported" in a human trafficking industry enslaving an estimated 40 million people. These nefarious industries rely on sophisticated money laundering schemes to operate. Despite tremendous resources dedicated to anti-money laundering (AML) only a tiny fraction of illicit activity is prevented. The research community can help. In this brief paper, we map the structural and behavioral dynamics driving the technical challenge. We review AML methods, current and emergent. We provide a first look at scalable graph convolutional neural networks for forensic analysis of financial data, which is massive, dense, and dynamic. We report preliminary experimental results using a large synthetic graph (1M nodes, 9M edges) generated by a data simulator we created called AMLSim. We consider opportunities for high performance efficiency, in terms of computation and memory, and we share results from a simple graph compression experiment. Our results support our working hypothesis that graph deep learning for AML bears great promise in the fight against criminal financial activity.

Connected through hyperedge HE-1 (induced by source paper "ocgnn one class classification with graph neural networks"):
  with members:
    - "fine grained event categorization with heterogeneous graph convolutional networks"
    - "deep learning on graphs a survey"
    - "how powerful are graph neural networks"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-2 (induced by source paper "tensor graph convolutional networks for prediction on dynamic graphs"):
  with members:
    - "dynamic graph convolutional networks"
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "stable tensor neural networks for rapid deep learning"
    - "community interaction and conflict on the web"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

Connected through hyperedge HE-3 (induced by source paper "identifying illicit accounts in large scale e payment networks a graph representation learning approach"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "deepinf social influence prediction with deep learning"
    - "edge2vec representation learning using edge semantics for biomedical knowledge discovery"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "deep sets"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "anti money laundering in bitcoin experimenting with graph convolutional networks for financial forensics"):
  with members:
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "graph convolutional neural networks for web scale recommender systems"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "the unreasonable effectiveness of address clustering"

Connected through hyperedge HE-5 (induced by source paper "towards federated graph learning for collaborative financial crimes detection"):
  with members:
    - "towards federated learning at scale system design"
    - "distributed edge partitioning for trillion edge graphs"
    - "federated learning strategies for improving communication efficiency"
    - "a hybrid approach to privacy preserving federated learning"
    - "practical secure aggregation for federated learning on user held data"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-6 (induced by source paper "exploring graph neural networks for stock market predictions with rolling window analysis"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "predicting stock market movements using network science an information theoretic approach"
    - "listening to chaotic whispers a deep learning framework for news oriented stock trend prediction"
    - "limits of end to end learning"
    - "representation learning on graphs methods and applications"
    - "semi supervised classification with graph convolutional networks"

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.SI(Social and Information Networks)