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: "hellrank a hellinger based centrality measure for bipartite social networks"
  Abstract: Measuring centrality in a social network, especially in bipartite mode, poses many challenges, for example, the requirement of full knowledge of the network topology, and the lack of properly detecting top-k behavioral representative users. To overcome the above mentioned challenges, we propose HellRank, an accurate centrality measure for identifying central nodes in bipartite social networks. HellRank is based on the Hellinger distance between two nodes on the same side of a bipartite network. We theoretically analyze the impact of this distance on a bipartite network and find upper and lower bounds for it. The computation of the HellRank centrality measure can be distributed, by letting each node uses local information only on its immediate neighbors. Consequently, one does not need a central entity that has full knowledge of the network topological structure. We experimentally evaluate the performance of the HellRank measure in correlation with other centrality measures on real-world networks. The results show partial ranking similarity between the HellRank and the other conventional metrics according to the Kendall and Spearman rank correlation coefficient.

Connected through hyperedge HE-1 (induced by source paper "change detection in noisy dynamic networks a spectral embedding approach"):
  with members:
    - "change detection in dynamic attributed networks"
    - "a probabilistic framework to node level anomaly detection in communication networks"
    - "dyngem deep embedding method for dynamic graphs"
    - "link prediction for interdisciplinary collaboration via co authorship network"
    - "fast change point detection on dynamic social networks"
    - "detection and localization of change points in temporal networks with the aid of stochastic block models"
    - "localization and centrality in networks"
    - "detecting change points in the large scale structure of evolving networks"

Connected through hyperedge HE-2 (induced by source paper "compressive closeness in networks"):
  with members:
    - "distributed algorithms for computation of centrality measures in complex networks"
    - "extracting implicit social relation for social recommendation techniques in user rating prediction"
    - "defining and evaluating network communities based on ground truth"
    - "sparse recovery with graph constraints fundamental limits and measurement construction"
    - "compressive sensing over graphs"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (induced by source paper "a matrix factorization model for hellinger based trust management in social internet of things"):
  with members:
    - "clarifying trust in social internet of things"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "extracting implicit social relation for social recommendation techniques in user rating prediction"
    - "collaborative deep learning for recommender systems"
    - "internet of things iot a vision architectural elements and future directions"

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)