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: "community detection in networks with unequal groups"
  Abstract: Recently, a phase transition has been discovered in the network community detection problem below which no algorithm can tell which nodes belong to which communities with success any better than a random guess. This result has, however, so far been limited to the case where the communities have the same size or the same average degree. Here we consider the case where the sizes or average degrees differ. This asymmetry allows us to assign nodes to communities with better-than-random success by examining their local neighborhoods. Using the cavity method, we show that this removes the detectability transition completely for networks with four groups or fewer, while for more than four groups the transition persists up to a critical amount of asymmetry but not beyond. The critical point in the latter case coincides with the point at which local information percolates, causing a global transition from a less-accurate solution to a more-accurate one.

Connected through hyperedge HE-1 (induced by source paper "self falsifiable hierarchical detection of overlapping communities on social networks"):
  with members:
    - "gemsec graph embedding with self clustering"
    - "hidden community detection in social networks"
    - "the ground truth about metadata and community detection in networks"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "spectral redemption in clustering sparse networks"
    - "demon a local first discovery method for overlapping communities"
    - "percolation computation in complex networks"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-2 (induced by source paper "community detection and improved detectability in multiplex networks"):
  with members:
    - "detectability thresholds of general modular graphs"
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "finding overlapping communities in multiplex networks"
    - "structure and inference in annotated networks"
    - "clustering network layers with the strata multilayer stochastic block model"
    - "community extraction in multilayer networks with heterogeneous community structure"
    - "information diffusion of topic propagation in social media"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-3 (induced by source paper "generative models for local network community detection"):
  with members:
    - "model selection for degree corrected block models"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "efficient and principled method for detecting communities in networks"

Connected through hyperedge HE-4 (induced by source paper "block models and personalized pagerank"):
  with members:
    - "find your place simple distributed algorithms for community detection"
    - "heat kernel based community detection"
    - "streaming graph partitioning in the planted partition model"
    - "pagerank beyond the web"
    - "exact recovery in the stochastic block model"
    - "spectral redemption in clustering sparse networks"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "community detection thresholds and the weak ramanujan property"

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)