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: "de anonymization of social networks with communities when quantifications meet algorithms"
  Abstract: A crucial privacy-driven issue nowadays is re-identifying anonymized social networks by mapping them to correlated cross-domain auxiliary networks. Prior works are typically based on modeling social networks as random graphs representing users and their relations, and subsequently quantify the quality of mappings through cost functions that are proposed without sufficient rationale. Also, it remains unknown how to algorithmically meet the demand of such quantifications, i.e., to find the minimizer of the cost functions. We address those concerns in a more realistic social network modeling parameterized by community structures that can be leveraged as side information for de-anonymization. By Maximum A Posteriori (MAP) estimation, our first contribution is new and well justified cost functions, which, when minimized, enjoy superiority to previous ones in finding the correct mapping with the highest probability. The feasibility of the cost functions is then for the first time algorithmically characterized. While proving the general multiplicative inapproximability, we are able to propose two algorithms, which, respectively, enjoy an \epsilon-additive approximation and a conditional optimality in carrying out successful user re-identification. Our theoretical findings are empirically validated, with a notable dataset extracted from rare true cross-domain networks that reproduce genuine social network de-anonymization. Both theoretical and empirical observations also manifest the importance of community information in enhancing privacy inferencing.

Connected through hyperedge HE-1 (induced by source paper "social community aware content placement in wireless device to device communication networks"):
  with members:
    - "leveraging social communities for optimizing cellular device to device communications"
    - "cooperative device to device communications with caching"
    - "mean field games for distributed caching in ultra dense small cell networks"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "context aware small cell networks how social metrics improve wireless resource allocation"
    - "social data offloading in d2d enhanced cellular networks by network formation games"
    - "cooperative game theoretic solution concepts for top k problems"
    - "a survey on device to device communication in cellular networks"

Connected through hyperedge HE-2 (induced by source paper "typicality matching for pairs of correlated graphs"):
  with members:
    - "seeded graph matching efficient algorithms and theoretical guarantees"
    - "exact alignment recovery for correlated erdos renyi graphs"
    - "optimal de anonymization in random graphs with community structure"

Connected through hyperedge HE-3 (induced by source paper "de anonymizing social networks with overlapping community structure"):
  with members:
    - "optimal de anonymization in random graphs with community structure"
    - "impact of clustering on the performance of network de anonymization"
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "exact recovery in the stochastic block model"
    - "defining and evaluating network communities based on ground truth"
    - "de anonymizing social networks"
    - "a path following algorithm for the graph matching problem"

Connected through hyperedge HE-4 (induced by source paper "seeded graph matching efficient algorithms and theoretical guarantees"):
  with members:
    - "an information theoretic framework for active de anonymization in social networks based on group memberships"
    - "optimal de anonymization in random graphs with community structure"

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)