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: "axiomatic ranking of network role similarity"
  Abstract: A key task in social network and other complex network analysis is role analysis: describing and categorizing nodes according to how they interact with other nodes. Two nodes have the same role if they interact with equivalent sets of neighbors. The most fundamental role equivalence is automorphic equivalence. Unfortunately, the fastest algorithms known for graph automorphism are nonpolynomial. Moreover, since exact equivalence may be rare, a more meaningful task is to measure the role similarity between any two nodes. This task is closely related to the structural or link-based similarity problem that SimRank attempts to solve. However, SimRank and most of its offshoots are not sufficient because they do not fully recognize automorphically or structurally equivalent nodes. In this paper we tackle two problems. First, what are the necessary properties for a role similarity measure or metric? Second, how can we derive a role similarity measure satisfying these properties? For the first problem, we justify several axiomatic properties necessary for a role similarity measure or metric: range, maximal similarity, automorphic equivalence, transitive similarity, and the triangle inequality. For the second problem, we present RoleSim, a new similarity metric with a simple iterative computational method. We rigorously prove that RoleSim satisfies all the axiomatic properties. We also introduce an iceberg RoleSim algorithm which can guarantee to discover all pairs with RoleSim score no less than a user-defined threshold $\theta$ without computing the RoleSim for every pair. We demonstrate the superior interpretative power of RoleSim on both both synthetic and real datasets.

Connected through hyperedge HE-1 (induced by source paper "realtime index free single source simrank processing on web scale graphs"):
  with members:
    - "prsim sublinear time simrank computation on large power law graphs"
    - "probesim scalable single source and top k simrank computations on dynamic graphs"
    - "sling a near optimal index structure for simrank"
    - "efficient simrank computation via linearization"

Connected through hyperedge HE-2 (induced by source paper "riwalk fast structural node embedding via role identification"):
  with members:
    - "regal representation learning based graph alignment"
    - "learning structural node embeddings via diffusion wavelets"
    - "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"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "prsim sublinear time simrank computation on large power law graphs"):
  with members:
    - "probesim scalable single source and top k simrank computations on dynamic graphs"
    - "sling a near optimal index structure for simrank"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "approximate personalized pagerank on dynamic graphs"
    - "panther fast top k similarity search in large networks"
    - "algorithmic complexity of power law networks"
    - "efficient simrank computation via linearization"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-4 (induced by source paper "search efficient binary network embedding"):
  with members:
    - "bernoulli embeddings for graphs"
    - "sine scalable incomplete network embedding"
    - "network representation learning a survey"
    - "hashnet deep learning to hash by continuation"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "dynamic discovery of type classes and relations in semantic web data"):
  with members:
    - "an efficient metric of automatic weight generation for properties in instance matching technique"
    - "simrank query rewriting through link analysis of the click graph"
    - "a fast general methodology for information theoretically optimal encodings of graphs"

Connected through hyperedge HE-6 (induced by source paper "meta path constrained random walk inference for large scale heterogeneous information networks"):
  with members:
    - "language models with transformers"
    - "world knowledge as indirect supervision for document clustering"

Connected through hyperedge HE-7 (induced by source paper "learning structural node embeddings via diffusion wavelets"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "inductive representation learning on large graphs"
    - "representation learning on graphs methods and applications"
    - "neural message passing for quantum chemistry"
    - "learning graph representations with embedding propagation"
    - "node2vec scalable feature learning for networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "struc2gauss structure preserving network embedding via gaussian embedding"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "preserving local and global information for network embedding"
    - "representation learning on graphs methods and applications"
    - "attributed network embedding for learning in a dynamic environment"
    - "node2vec scalable feature learning for networks"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "deepwalk online learning of social representations"
    - "word representations via gaussian 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)