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: "on the bursty evolution of online social networks"
  Abstract: The high level of dynamics in today's online social networks (OSNs) creates new challenges for their infrastructures and providers. In particular, dynamics involving edge creation has direct implications on strategies for resource allocation, data partitioning and replication. Understanding network dynamics in the context of physical time is a critical first step towards a predictive approach towards infrastructure management in OSNs. Despite increasing efforts to study social network dynamics, current analyses mainly focus on change over time of static metrics computed on snapshots of social graphs. The limited prior work models network dynamics with respect to a logical clock. In this paper, we present results of analyzing a large timestamped dataset describing the initial growth and evolution of Renren, the leading social network in China. We analyze and model the burstiness of link creation process, using the second derivative, i.e. the acceleration of the degree. This allows us to detect bursts, and to characterize the social activity of a OSN user as one of four phases: acceleration at the beginning of an activity burst, where link creation rate is increasing; deceleration when burst is ending and link creation process is slowing; cruising, when node activity is in a steady state, and complete inactivity.

Connected through hyperedge HE-1 (induced by source paper "detecting community structure in dynamic social networks using the concept of leadership"):
  with members:
    - "clustering evolving networks"
    - "the bursty dynamics of the twitter information network"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "sequential detection of temporal communities by estrangement confinement"
    - "listing all maximal cliques in sparse graphs in near optimal time"
    - "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 "systems for near real time analysis of large scale dynamic graphs"):
  with members:
    - "blinkdb queries with bounded errors and bounded response times on very large data"
    - "efficient subgraph matching on billion node graphs"
    - "an empirical study and analysis of the dynamic load balancing techniques used in parallel computing systems"
    - "time varying graphs and social network analysis temporal indicators and metrics"

Connected through hyperedge HE-3 (induced by source paper "social resilience in online communities the autopsy of friendster"):
  with members:
    - "modeling the adoption of innovations in the presence of geographic and media influences"
    - "defining and evaluating network communities based on ground truth"
    - "partisan asymmetries in online political activity"
    - "measuring tie strength in implicit social networks"

Connected through hyperedge HE-4 (induced by source paper "temporal analysis of activity patterns of editors in collaborative mapping project of openstreetmap"):
  with members:
    - "analyzing the performance of lru caches under non stationary traffic patterns"
    - "a practical approach to language complexity a wikipedia case study"
    - "rhythms of social interaction messaging within a massive online network"

Connected through hyperedge HE-5 (induced by source paper "google or google dissecting the evolution of the new osn in its first year"):
  with members:
    - "multi scale dynamics in a massive online social network"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "four degrees of separation"

Connected through hyperedge HE-6 (induced by source paper "google or google examining the popularity of the new osn"):
  with members:
    - "multi scale dynamics in a massive online social network"
    - "the anatomy of the facebook social graph"

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)