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: "topological features of online social networks"
  Abstract: The importance of modeling and analyzing Social Networks is a consequence of the success of Online Social Networks during last years. Several models of networks have been proposed, reflecting the different characteristics of Social Networks. Some of them fit better to model specific phenomena, such as the growth and the evolution of the Social Networks; others are more appropriate to capture the topological characteristics of the networks. Because these networks show unique and different properties and features, in this work we describe and exploit several models in order to capture the structure of popular Online Social Networks, such as Arxiv, Facebook, Wikipedia and YouTube. Our experimentation aims at verifying the structural characteristics of these networks, in order to understand what model better depicts their structure, and to analyze the inner community structure, to illustrate how members of these Online Social Networks interact and group together into smaller communities.

Connected through hyperedge HE-1 (induced by source paper "network signatures from image representation of adjacency matrices deep transfer learning for subgraph classification"):
  with members:
    - "node by node greedy deep learning for interpretable features"
    - "caffe convolutional architecture for fast feature embedding"
    - "defining and evaluating network communities based on ground truth"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-2 (induced by source paper "sample nlpde and nlode social media modeling of information transmission for infectious diseases case study ebola"):
  with members:
    - "modeling information diffusion in online social networks with partial differential equations"
    - "analysis of a heterogeneous social network of humans and cultural objects"
    - "a computer virus propagation model using delay differential equations with probabilistic contagion and immunity"
    - "inheritance patterns in citation networks reveal scientific memes"
    - "statistically significant detection of linguistic change"
    - "do retweets indicate interest trust agreement extended abstract"
    - "a cooperate defect model for the spread of deviant behavior in social networks"
    - "what they do in shadows twitter underground follower market"

Connected through hyperedge HE-3 (induced by source paper "analysis of a heterogeneous social network of humans and cultural objects"):
  with members:
    - "people are strange when you re a stranger impact and influence of bots on social networks"
    - "the digital evolution of occupy wall street"
    - "the geospatial characteristics of a social movement communication network"
    - "analyzing user behavior across social sharing environments"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "a large scale community structure analysis in facebook"
    - "crawling facebook for social network analysis purposes"
    - "multidimensional social network in the social recommender system"

Connected through hyperedge HE-4 (induced by source paper "detecting criminal organizations in mobile phone networks"):
  with members:
    - "the digital evolution of occupy wall street"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "the geospatial characteristics of a social movement communication network"
    - "crawling facebook for social network analysis purposes"

Connected through hyperedge HE-5 (induced by source paper "forensic analysis of phone call networks"):
  with members:
    - "communities and hierarchical structures in dynamic social networks analysis and visualization"
    - "a novel measure of edge centrality in social networks"
    - "a classification for community discovery methods in complex networks"
    - "generalized louvain method for community detection in large 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)