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: "estimating network parameters for selecting community detection algorithms"
  Abstract: This paper considers the problem of algorithm selection for community detection. The aim of community detection is to identify sets of nodes in a network which are more interconnected relative to their connectivity to the rest of the network. A large number of algorithms have been developed to tackle this problem, but as with any machine learning task there is no "one-size-fits-all" and each algorithm excels in a specific part of the problem space. This paper examines the performance of algorithms developed for weighted networks against those using unweighted networks for different parts of the problem space (parameterised by the intra/inter community links). It is then demonstrated how the choice of algorithm (weighted/unweighted) can be made based only on the observed network.

Connected through hyperedge HE-1 (induced by source paper "an exact no free lunch theorem for community detection"):
  with members:
    - "metrics matter in community detection"
    - "evaluation of community structures using kappa index and f score instead of normalized mutual information"
    - "the ground truth about metadata and community detection in networks"
    - "average stability is invariant to data preconditioning implications to exp concave empirical risk minimization"
    - "on the relationship between gaussian stochastic blockmodels and label propagation algorithms"

Connected through hyperedge HE-2 (induced by source paper "the ground truth about metadata and community detection in networks"):
  with members:
    - "detectability of small communities in multilayer and temporal networks eigenvector localization layer aggregation and time series discretization"
    - "structure and inference in annotated networks"
    - "efficiently inferring community structure in bipartite networks"
    - "spectral redemption in clustering sparse networks"
    - "community detection thresholds and the weak ramanujan property"
    - "defining and evaluating network communities based on ground truth"
    - "topological feature based classification"
    - "efficient and principled method for detecting communities in networks"

Connected through hyperedge HE-3 (induced by source paper "groups net group meetings aware routing in multi hop d2d networks"):
  with members:
    - "group mobility detection tracking and characterization"
    - "a survey on device to device communication in cellular networks"
    - "an overview on 3gpp device to device proximity services"
    - "on the dynamics of human proximity for data diffusion in ad hoc networks"
    - "swim a simple model to generate small mobile worlds"

Connected through hyperedge HE-4 (induced by source paper "group mobility detection tracking and characterization"):
  with members:
    - "impact investigation of mobile user patterns across university campuses using wlan trace analysis"

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.SI(Social and Information Networks)