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: "mining behavioral groups in large wireless lans"
  Abstract: One vision of future wireless networks is that they will be deeply integrated and embedded in our lives and will involve the use of personalized mobile devices. User behavior in such networks is bound to affect the network performance. It is imperative to study and characterize the fundamental structure of wireless user behavior in order to model, manage, leverage and design efficient mobile networks. It is also important to make such study as realistic as possible, based on extensive measurements collected from existing deployed wireless networks. #R##N#In this study, using our systematic TRACE approach, we analyze wireless users' behavioral patterns by extensively mining wireless network logs from two major university campuses. We represent the data using location preference vectors, and utilize unsupervised learning (clustering) to classify trends in user behavior using novel similarity metrics. Matrix decomposition techniques are used to identify (and differentiate between) major patterns. While our findings validate intuitive repetitive behavioral trends and user grouping, it is surprising to find the qualitative commonalities of user behaviors from the two universities. We discover multi-modal user behavior for more than 60% of the users, and there are hundreds of distinct groups with unique behavioral patterns in both campuses. The sizes of the major groups follow a power-law distribution. Our methods and findings provide an essential step towards network management and behavior-aware network protocols and applications, to name a few.

Connected through hyperedge HE-1 (induced by source paper "device fingerprinting in wireless networks challenges and opportunities"):
  with members:
    - "an empirical study of passive 802 11 device fingerprinting"
    - "fingerprints in the ether using the physical layer for wireless authentication"
    - "using the physical layer for wireless authentication in time variant channels"

Connected through hyperedge HE-2 (induced by source paper "similarity analysis and modeling in mobile societies the missing link"):
  with members:
    - "protect proximity based trust advisor using encounters for mobile societies"
    - "csi a paradigm for behavior oriented delivery services in mobile human networks"
    - "modeling spatial and temporal dependencies of user mobility in wireless mobile networks"

Connected through hyperedge HE-3 (induced by source paper "understanding periodicity and regularity of nodal encounters in mobile networks a spectral analysis"):
  with members:
    - "modeling spatial and temporal dependencies of user mobility in wireless mobile networks"
    - "encounter based worms analysis and defense"

Connected through hyperedge HE-4 (induced by source paper "analysis of spatio temporal preferences and encounter statistics for dtn performance"):
  with members:
    - "csi a paradigm for behavior oriented delivery services in mobile human networks"
    - "modeling spatial and temporal dependencies of user mobility in wireless mobile networks"

Connected through hyperedge HE-5 (induced by source paper "shield social sensing and help in emergency using mobile devices"):
  with members:
    - "csi a paradigm for behavior oriented delivery services in mobile human networks"
    - "modeling spatial and temporal dependencies of user mobility in wireless mobile networks"

Connected through hyperedge HE-6 (induced by source paper "data driven co clustering model of internet usage in large mobile societies"):
  with members:
    - "protect proximity based trust advisor using encounters for mobile societies"
    - "modeling spatial and temporal dependencies of user mobility in wireless mobile networks"

Connected through hyperedge HE-7 (induced by source paper "csi a paradigm for behavior oriented delivery services in mobile human networks"):
  with members:
    - "evaluating mobility pattern space routing for dtns"

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)