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: "search in power law networks"
  Abstract: Many communication and social networks have power-law link distributions, containing a few nodes that have a very high degree and many with low degree. The high connectivity nodes play the important role of hubs in communication and networking, a fact that can be exploited when designing efficient search algorithms. We introduce a number of local search strategies that utilize high degree nodes in power-law graphs and that have costs scaling sublinearly with the size of the graph. We also demonstrate the utility of these strategies on the GNUTELLA peer-to-peer network.

Connected through hyperedge HE-1 (induced by source paper "a general centrality framework based on node navigability"):
  with members:
    - "analysis of a heterogeneous social network of humans and cultural objects"
    - "axioms for centrality"
    - "total communicability as a centrality measure"

Connected through hyperedge HE-2 (induced by source paper "efficient network navigation with partial information"):
  with members:
    - "graph filters and the z laplacian"

Connected through hyperedge HE-3 (induced by source paper "learning representations of graph data a survey"):
  with members:
    - "fast sequence based embedding with diffusion graphs"
    - "adaptive graph convolutional neural networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "deriving neural architectures from sequence and graph kernels"
    - "inductive representation learning on large graphs"

Connected through hyperedge HE-4 (induced by source paper "diffusion on dynamic contact networks with indirect transmission links"):
  with members:
    - "lightweight mobile ad hoc network routing protocols for smartphones"
    - "impact of indirect contacts in emerging infectious disease on social networks"
    - "least cost influence maximization across multiple social networks"
    - "diffusion in colocation contact networks the impact of nodal spatiotemporal dynamics"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "common neighbours and the local community paradigm for topological link prediction in bipartite networks"
    - "latent self exciting point process model for spatial temporal networks"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-5 (induced by source paper "cross domain network representations"):
  with members:
    - "a framework of transferring structures across large scale information networks"
    - "struc2vec learning node representations from structural identity"
    - "node2vec scalable feature learning for networks"
    - "deep feature learning with relative distance comparison for person re identification"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "representation learning a review and new perspectives"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-6 (induced by source paper "star sampling with and without replacement"):
  with members:
    - "a markov chain model for the search time for max degree nodes in a graph using a biased random walk"
    - "a survey and taxonomy of graph sampling"
    - "quick detection of nodes with large degrees"
    - "walking on a graph with a magnifying glass stratified sampling via weighted random walks"
    - "benefits of bias towards better characterization of network sampling"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-7 (induced by source paper "on the estimation and use of statistical modelling in information retrieval"):
  with members:
    - "part of speech based term weighting for information retrieval"
    - "social resilience in online communities the autopsy of friendster"
    - "vocabulary growth in collaborative tagging systems"
    - "collaborative thesaurus tagging the wikipedia way"
    - "the structure of broad topics on the web"

Connected through hyperedge HE-8 (induced by source paper "what do your friends think efficient polling methods for networks using friendship paradox"):
  with members:
    - "information diffusion in social networks friendship paradox based models and statistical inference"
    - "friendship paradox biases perceptions in directed networks"
    - "centrality friendship paradoxes when our friends are more important than us"
    - "gemsec graph embedding with self clustering"
    - "the majority illusion in social networks"
    - "qualities and inequalities in online social networks through the lens of the generalized friendship paradox"
    - "online reputation and polling systems data incest social learning and revealed preferences"
    - "generalized friendship paradox in complex networks the case of scientific collaboration"

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)