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: "predicting community evolution in social networks"
  Abstract: Nowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains. These evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods: Stable Group Changes Identification (SGCI) and Group Evolution Discovery (GED). Based on the observed evolution chains of various length, structural network features are extracted, validated and selected as well as used to learn classification models. The experimental studies were performed on three real datasets with different profile: DBLP, Facebook and Polish blogosphere. The process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length. The results revealed that, in general, the longer evolution chains the better predictive abilities of the classification models. However, chains of length 3 to 7 enabled the GED-based method to almost reach its maximum possible prediction quality. For SGCI, this value was at the level of 3–5 last periods.

Connected through hyperedge HE-1 (induced by source paper "community discovery in dynamic networks a survey"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "revealing evolutions in dynamical networks"
    - "the ground truth about metadata and community detection in networks"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "a framework for the construction of generative models for mesoscale structure in multilayer networks"
    - "generative benchmark models for mesoscale structures in multilayer networks"
    - "enumerating maximal cliques in temporal graphs"
    - "using multiple criteria methods to evaluate community partitions"

Connected through hyperedge HE-2 (induced by source paper "temporally evolving community detection and prediction in content centric networks"):
  with members:
    - "predicting small group accretion in social networks a topology based incremental approach"
    - "detecting overlapping temporal community structure in time evolving networks"
    - "different approaches to community evolution prediction in blogosphere"
    - "sequential detection of temporal communities by estrangement confinement"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-3 (induced by source paper "temporally evolving community detection and prediction in content centric networks"):
  with members:
    - "predicting small group accretion in social networks a topology based incremental approach"
    - "detecting overlapping temporal community structure in time evolving networks"
    - "different approaches to community evolution prediction in blogosphere"
    - "sequential detection of temporal communities by estrangement confinement"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-4 (induced by source paper "analysis of group evolution prediction in complex networks"):
  with members:
    - "a comparative study of different approaches for tracking communities in evolving social networks"
    - "community discovery in dynamic networks a survey"
    - "wordnet2vec corpora agnostic word vectorization method"
    - "estimating continuous distributions in bayesian classifiers"
    - "different approaches to community evolution prediction in blogosphere"
    - "ged the method for group evolution discovery in social networks"
    - "identification of group changes in blogosphere"
    - "predicting group evolution in the social network"

Connected through hyperedge HE-5 (induced by source paper "do we really need to catch them all a new user guided social media crawling method"):
  with members:
    - "finding influential users in social media using association rule learning"
    - "sampling online social networks via heterogeneous statistics"
    - "crawling facebook for social network analysis purposes"
    - "multigraph sampling of online social networks"

Connected through hyperedge HE-6 (induced by source paper "finding influential users in social media using association rule learning"):
  with members:
    - "measuring user influence on twitter"
    - "key user extraction based on telecommunication data aka key users in social network how to find them"
    - "the multidimensional study of viral campaigns as branching processes"
    - "predicting influential users in online social networks"

Connected through hyperedge HE-7 (induced by source paper "dynamic clustering in social networks using louvain and infomap method"):
  with members:
    - "multiscale network generation"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "rhythms of social interaction messaging within a massive online network"

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)