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: "tensor based link prediction in intermittently connected wireless networks"
  Abstract: Through several studies, it has been highlighted that mobility patterns in mobile networks are driven by human behaviors. This effect has been particularly observed in intermittently connected networks like DTN (Delay Tolerant Networks). Given that common social intentions generate similar human behavior, it is relevant to exploit this knowledge in the network protocols design, e.g. to identify the closeness degree between two nodes. In this paper, we propose a temporal link prediction technique for DTN which quantifies the behavior similarity between each pair of nodes and makes use of it to predict future links. Our prediction method keeps track of the spatio-temporal aspects of nodes behaviors organized as a third-order tensor that aims to records the evolution of the network topology. After collapsing the tensor information, we compute the degree of similarity for each pair of nodes using the Katz measure. This metric gives us an indication on the link occurrence between two nodes relying on their closeness. We show the efficiency of this method by applying it on three mobility traces: two real traces and one synthetic trace. Through several simulations, we demonstrate the effectiveness of the technique regarding another approach based on a similarity metric used in DTN. The validity of this method is proven when the computation of score is made in a distributed way (i.e. with local information). We attest that the tensor-based technique is effective for temporal link prediction applied to the intermittently connected networks. Furthermore, we think that this technique can go beyond the realm of DTN and we believe this can be further applied on every case of figure in which there is a need to derive the underlying social structure of a network of mobile users.

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:
    - "pagerank standing on the shoulders of giants"
    - "similarity analysis and modeling in mobile societies the missing link"
    - "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)