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: "rhythms of social interaction messaging within a massive online network"
  Abstract: College students spend a significant amount of time using online social net- work services for messaging, sharing information, and keeping in touch with one another (e.g. [3, 10]). As these services represent a plentiful source of electronic data, they provide an opportunity to study dynamic patterns of social interactions quickly and exhaustively. In this paper, we study the social net- work service Facebook, which began in early 2004 in select universities, but grew quickly to encompass a very large number of universities. Studies have shown that, as of 2006, Facebook use is nearly ubiquitous among U. S. college students with over 90% active participation among undergraduates [5, 16].

Connected through hyperedge HE-1 (induced by source paper "designing for health chatbots"):
  with members:
    - "confiding in and listening to virtual agents the effect of personality"
    - "evaluating quality of chatbots and intelligent conversational agents"
    - "hierarchical recurrent attention network for response generation"
    - "bots as virtual confederates design and ethics"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "adversarial evaluation of dialogue models"
    - "a survey of available corpora for building data driven dialogue systems"
    - "neural responding machine for short text conversation"

Connected through hyperedge HE-2 (induced by source paper "driving the last mile characterizing and understanding distracted driving posts on social networks"):
  with members:
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "aggregated residual transformations for deep neural networks"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "reliability of relational event model estimates under sampling how to fit a relational event model to 360 million dyadic events"):
  with members:
    - "extended dynamic subgraph statistics using h index parameterized data structures"

Connected through hyperedge HE-4 (induced by source paper "offline behaviors of online friends"):
  with members:
    - "inferring person to person proximity using wifi signals"
    - "tracking human mobility using wifi signals"
    - "privacy in sensor driven human data collection a guide for practitioners"

Connected through hyperedge HE-5 (induced by source paper "dynamic clustering in social networks using louvain and infomap method"):
  with members:
    - "predicting community evolution in social networks"
    - "multiscale network generation"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-6 (induced by source paper "competition between homophily and information entropy maximization in social networks"):
  with members:
    - "cumulative effect in information diffusion empirical study on a microblogging network"
    - "estimating and sampling graphs with multidimensional random walks"
    - "weak ties subtle role of information diffusion in online social networks"
    - "distance is not dead social interaction and geographical distance in the internet era"

Connected through hyperedge HE-7 (induced by source paper "special issue on foundations of social computing enabling social applications via decentralized social data management"):
  with members:
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-8 (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"
    - "on the bursty evolution of online social networks"
    - "a practical approach to language complexity a wikipedia case study"

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.CY(Computers and Society)