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: "detecting communities under differential privacy"
  Abstract: Complex networks usually expose community structure with groups of nodes sharing many links with the other nodes in the same group and relatively few with the nodes of the rest. This feature captures valuable information about the organization and even the evolution of the network. Over the last decade, a great number of algorithms for community detection have been proposed to deal with the increasingly complex networks. However, the problem of doing this in a private manner is rarely considered. In this paper, we solve this problem under differential privacy, a prominent privacy concept for releasing private data. We analyze the major challenges behind the problem and propose several schemes to tackle them from two perspectives: input perturbation and algorithm perturbation. We choose Louvain method as the back-end community detection for input perturbation schemes and propose the method LouvainDP which runs Louvain algorithm on a noisy super-graph. For algorithm perturbation, we design ModDivisive using exponential mechanism with the modularity as the score. We have thoroughly evaluated our techniques on real graphs of different sizes and verified their outperformance over the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "publishing community preserving attributed social graphs with a differential privacy guarantee"):
  with members:
    - "robust active attacks on social graphs"
    - "community detection through likelihood optimization in search of a sound model"
    - "using node centrality and optimal control to maximize information diffusion in social networks"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "a scalable generative graph model with community structure"
    - "community detection in networks with node attributes"
    - "differentially private data analysis of social networks via restricted sensitivity"
    - "preserving link privacy in social network based systems"

Connected through hyperedge HE-2 (induced by source paper "dpugc learn differentially private representation for user generated contents"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "shaped shared private encoder decoder for text style adaptation"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"
    - "enriching word vectors with subword information"
    - "nonparametric spherical topic modeling with word embeddings"
    - "document classification by inversion of distributed language representations"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "differentially private two party egocentric betweenness centrality"):
  with members:
    - "efficient lipschitz extensions for high dimensional graph statistics and node private degree distributions"
    - "mining frequent graph patterns with differential privacy"
    - "link prediction by de anonymization how we won the kaggle social network challenge"
    - "de anonymizing social 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)