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: "from community to role based graph embeddings"
  Abstract: Roles are sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities are sets of nodes with more connections inside the set than outside (based on proximity/closeness, density). Roles and communities are fundamentally different but important complementary notions. Recently, the notion of roles has become increasingly important and has gained a lot of attention due to the proliferation of work on learning representations (node/edge embeddings) from graphs that preserve the notion of roles. Unfortunately, recent work has sometimes confused the notion of roles and communities leading to misleading or incorrect claims about the capabilities of network embedding methods. As such, this manuscript seeks to clarify the differences between roles and communities, and formalize the general mechanisms (e.g., random walks, feature diffusion) that give rise to community or role-based embeddings. We show mathematically why embedding methods based on these identified mechanisms are either community or role-based. These mechanisms are typically easy to identify and can help researchers quickly determine whether a method is more prone to learn community or role-based embeddings. Furthermore, they also serve as a basis for developing new and better methods for community or role-based embeddings. Finally, we analyze and discuss the applications and data characteristics where community or role-based embeddings are most appropriate.

Connected through hyperedge HE-1 (induced by source paper "scalable dyadic independence models with local and global constraints"):
  with members:
    - "evalne a framework for evaluating network embeddings on link prediction"
    - "sample optimal low rank approximation of distance matrices"
    - "struc2vec learning node representations from structural identity"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "deep graph similarity learning a survey"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "deep graph matching consensus"
    - "dynamic graph convolutional networks"
    - "glmnet graph learning matching networks for feature matching"
    - "learning combinatorial embedding networks for deep graph matching"
    - "community preserving graph convolutions for structural and functional joint embedding of brain networks"
    - "generalizing from a few examples a survey on few shot learning"
    - "rethinking kernel methods for node representation learning on graphs"

Connected through hyperedge HE-3 (induced by source paper "on the equivalence between node embeddings and structural graph representations"):
  with members:
    - "graph representation ensemble learning"
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "on the limitations of representing functions on sets"
    - "position aware graph neural networks"
    - "what can neural networks reason about"
    - "correlated variational auto encoders"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "temporal network sampling"):
  with members:
    - "temporal network representation learning"
    - "dynamic joint variational graph autoencoders"
    - "influential user subscription on time decaying social streams"
    - "explanations for temporal recommendations"
    - "sampling for approximate bipartite network projection"
    - "on sampling from massive graph streams"
    - "higher order organization of complex networks"
    - "role discovery in 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)