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: "similarity analysis and modeling in mobile societies the missing link"
  Abstract: A new generation of "behavior-aware" delay tolerant networks is emerging in what may define future mobile social networks. With the introduction of novel behavior-aware protocols, services and architectures, there is a pressing need to understand and realistically model mobile users behavioral characteristics, their similarity and clustering. Such models are essential for the analysis, performance evaluation, and simulation of future DTNs. This paper addresses issues related to mobile user similarity, its definition, analysis and modeling. To define similarity, we adopt a behavioral-profile based on users location preferences using their on-line association matrix and its SVD, then calculate the behavioral distance to capture user similarity. This measures the difference of the major spatio-temporal behavioral trends and can be used to cluster users into similarity groups or communities. We then analyze and contrast similarity distributions of mobile user populations in two settings: (i) based on real measurements from four major campuses with over ten thousand users for a month, and (ii) based on existing mobility models, including random direction and time-varying community models. Our results show a rich set of similar communities in real mobile societies with distinct behavioral clusters of users. This is true for all the traces studied, with the trend being consistent over time. Surprisingly, however, we find that the existing mobility models do not explicitly capture similarity and result in homogeneous users that are all similar to each other. Thus the richness and diversity of user behavioral patterns is not captured to any degree in the existing models. These findings strongly suggest that similarity should be explicitly captured in future mobility models, which motivates the need to re-visit mobility modeling in the future.

Connected through hyperedge HE-1 (induced by source paper "improving link prediction in intermittently connected wireless networks by considering link and proximity stabilities"):
  with members:
    - "tensor based link prediction in intermittently connected wireless networks"
    - "pagerank standing on the shoulders of giants"
    - "modeling spatial and temporal dependencies of user mobility in wireless mobile networks"

Connected through hyperedge HE-2 (induced by source paper "tracking topology dynamicity for link prediction in intermittently connected wireless networks"):
  with members:
    - "pagerank standing on the shoulders of giants"
    - "modeling spatial and temporal dependencies of user mobility in wireless mobile networks"

Connected through hyperedge HE-3 (induced by source paper "tensor based link prediction in intermittently connected wireless networks"):
  with members:
    - "modeling spatial and temporal dependencies of user mobility in wireless mobile 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.NI(Networking and Internet Architecture)