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: "node representation learning for directed graphs"
  Abstract: We propose a novel approach for learning node representations in directed graphs, which maintains separate views or embedding spaces for the two distinct node roles induced by the directionality of the edges. In order to achieve this, we propose an alternating random walk strategy to generate training samples from the directed graph while preserving the role information. These samples are then trained with the objective of preserving the likelihood of node neighborhoods with nodes retaining their source/target semantics. We also unearth the limitations of evaluations on directed graphs in previous works and propose a clear strategy for evaluating link prediction and graph reconstruction in directed graphs. We conduct extensive experiments to showcase our effectiveness on several real-world datasets on link prediction, multi-label classification and graph reconstruction tasks. We show that the embeddings from our approach are indeed robust, generalizable and well performing across multiple kinds of tasks and graphs. We show that we consistently outperform all baselines for multilabel node classification task. In addition to providing a theoretical interpretation of our method we also show that we are considerably more robust than the other directed graph approaches.

Connected through hyperedge HE-1 (induced by source paper "finding interpretable concept spaces in node embeddings using knowledge bases"):
  with members:
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "why should i trust you explaining the predictions of any classifier"
    - "entity embeddings with conceptual subspaces as a basis for plausible reasoning"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "investigating extensions to random walk based graph embedding"):
  with members:
    - "shortest path distance approximation using deep learning techniques"
    - "community aware random walk for network embedding"
    - "graph embedding techniques applications and performance a survey"
    - "harp hierarchical representation learning for networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "don t walk skip online learning of multi scale network embeddings"
    - "line large scale information network embedding"

Connected through hyperedge HE-3 (induced by source paper "low dimensional statistical manifold embedding of directed graphs"):
  with members:
    - "verse versatile graph embeddings from similarity measures"
    - "representation learning on graphs methods and applications"
    - "node2vec scalable feature learning for networks"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "efficient estimation of mutual information for strongly dependent variables"
    - "word representations via gaussian embedding"
    - "model of complex networks based on citation dynamics"

Connected through hyperedge HE-4 (induced by source paper "a comparative study for unsupervised network representation learning"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "pitfalls of graph neural network evaluation"
    - "verse versatile graph embeddings from similarity measures"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"

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)