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: "social resilience in online communities the autopsy of friendster"
  Abstract: We empirically analyze five online communities: Friendster, Livejournal, Facebook, Orkut, and Myspace, to study how social networks decline. We define social resilience as the ability of a community to withstand changes. We do not argue about the cause of such changes, but concentrate on their impact. Changes may cause users to leave, which may trigger further leaves of others who lost connection to their friends. This may lead to cascades of users leaving. A social network is said to be resilient if the size of such cascades can be limited. To quantify resilience, we use the k-core analysis, to identify subsets of the network in which all users have at least k friends. These connections generate benefits (b) for each user, which have to outweigh the costs (c) of being a member of the network. If this difference is not positive, users leave. After all cascades, the remaining network is the k-core of the original network determined by the cost-to-benefit (c/b) ratio. By analysing the cumulative distribution of k-cores we are able to calculate the number of users remaining in each community. This allows us to infer the impact of the c/b ratio on the resilience of these online communities. We find that the different online communities have different k-core distributions. Consequently, similar changes in the c/b ratio have a different impact on the amount of active users. Further, our resilience analysis shows that the topology of a social network alone cannot explain its success of failure. As a case study, we focus on the evolution of Friendster. We identify time periods when new users entering the network observed an insufficient c/b ratio. This measure can be seen as a precursor of the later collapse of the community. Our analysis can be applied to estimate the impact of changes in the user interface, which may temporarily increase the c/b ratio, thus posing a threat for the community to shrink, or even to collapse.

Connected through hyperedge HE-1 (induced by source paper "span core decomposition for temporal networks algorithms and applications"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "a survey of community search over big graphs"
    - "finding events in temporal networks segmentation meets densest subgraph discovery"
    - "graph embedding techniques applications and performance a survey"
    - "on finding dense common subgraphs"
    - "listing all maximal k plexes in temporal graphs"
    - "mining maximal span cores from temporal networks"
    - "to be connected or not to be connected that is the minimum inefficiency subgraph problem"

Connected through hyperedge HE-2 (induced by source paper "distance generalized core decomposition"):
  with members:
    - "mining maximal span cores from temporal networks"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "density friendly graph decomposition"
    - "the minimum wiener connector problem"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "truss decomposition in massive networks"
    - "four degrees of separation"

Connected through hyperedge HE-3 (induced by source paper "k core minimization a game theoretic approach"):
  with members:
    - "towards scalable network delay minimization"
    - "boosting information spread an algorithmic approach"
    - "preventing unraveling in social networks gets harder"
    - "parameterized exact and approximation algorithms for maximum k set cover and related satisfiability problems"
    - "graph measures and network robustness"
    - "a model of internet topology using k shell decomposition"

Connected through hyperedge HE-4 (induced by source paper "to act or react investigating proactive strategies for online community moderation"):
  with members:
    - "the effect of extremist violence on hateful speech online"
    - "on the origins of memes by means of fringe web communities"
    - "automated hate speech detection and the problem of offensive language"
    - "measuring offensive speech in online political discourse"
    - "online political discourse in the trump era"
    - "surviving an eternal september how an online community managed a surge of newcomers"

Connected through hyperedge HE-5 (induced by source paper "on the estimation and use of statistical modelling in information retrieval"):
  with members:
    - "part of speech based term weighting for information retrieval"
    - "vocabulary growth in collaborative tagging systems"
    - "collaborative thesaurus tagging the wikipedia way"
    - "the structure of broad topics on the web"
    - "search in power law networks"

Connected through hyperedge HE-6 (induced by source paper "modeling user reputation in online social networks the role of costs benefits and reciprocity"):
  with members:
    - "nepotistic relationships in twitter and their impact on rank prestige algorithms"

Connected through hyperedge HE-7 (induced by source paper "core decomposition in multilayer networks theory algorithms and applications"):
  with members:
    - "on finding dense common subgraphs"
    - "mining maximal span cores from temporal networks"
    - "to be connected or not to be connected that is the minimum inefficiency subgraph problem"
    - "diversified coherent core search on multi layer graphs"
    - "best friends forever bff finding lasting dense subgraphs"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "a fast order based approach for core maintenance"
    - "i o efficient core graph decomposition at web scale"

Connected through hyperedge HE-8 (induced by source paper "mining maximal span cores from temporal networks"):
  with members:
    - "best friends forever bff finding lasting dense subgraphs"
    - "enumerating maximal cliques in temporal graphs"
    - "computing maximal cliques in link streams"
    - "prediction of emerging technologies based on analysis of the us patent citation network"
    - "dense subgraph maintenance under streaming edge weight updates for real time story identification"
    - "listing all maximal cliques in sparse graphs in near optimal time"

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)