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: "efficiently inferring community structure in bipartite networks"
  Abstract: Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of information through one-mode projections, and lack of interpretability. Here we solve the community detection problem for bipartite networks by formulating a bipartite stochastic block model, which explicitly includes vertex type information and may be trivially extended to k-partite networks. This bipartite stochastic block model yields a projection-free and statistically principled method for community detection that makes clear assumptions and parameter choices and yields interpretable results. We demonstrate this model’s ability to efficiently and accurately find community structure in synthetic bipartite networks with known structure and in real-world bipartite networks with unknown structure, and we characterize its performance in practical contexts.

Connected through hyperedge HE-1 (induced by source paper "extracting hierarchical backbones from bipartite networks"):
  with members:
    - "extracting tag hierarchies"

Connected through hyperedge HE-2 (induced by source paper "algorithmic detectability threshold of the stochastic block model"):
  with members:
    - "detectability thresholds of general modular graphs"
    - "structure and inference in annotated networks"
    - "estimating the number of communities in a network"
    - "identification of core periphery structure in networks"
    - "detectability of the spectral method for sparse graph partitioning"
    - "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-3 (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"
    - "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"
    - "estimating network parameters for selecting community detection algorithms"

Connected through hyperedge HE-4 (induced by source paper "structure and inference in annotated networks"):
  with members:
    - "clustering attributed graphs models measures and methods"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "semi supervised eigenvectors for large scale locally biased learning"
    - "community structure detection in complex networks with partial background information"
    - "community detection in networks with node attributes"
    - "friendship networks and social status"
    - "social structure of facebook networks"
    - "active learning for node classification in assortative and disassortative networks"

Connected through hyperedge HE-5 (induced by source paper "on equivalence of likelihood maximization of stochastic block model and constrained nonnegative matrix factorization"):
  with members:
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "community detection in bipartite networks using weighted symmetric binary matrix factorization"
    - "overlapping community detection in networks with positive and negative links"
    - "spectral redemption in clustering sparse networks"
    - "overlapping community detection in complex networks using symmetric binary matrix factorization"

Connected through hyperedge HE-6 (induced by source paper "ground truth concept based communities versus the external classification of physics manuscripts"):
  with members:
    - "structure and inference in annotated networks"
    - "clustering attributed graphs models measures and methods"
    - "defining and evaluating network communities based on ground truth"
    - "a new methodology for constructing a publication level classification system of science"

Connected through hyperedge HE-7 (induced by source paper "quantitative function and algorithm for community detection in bipartite networks"):
  with members:
    - "local community extraction in directed networks"
    - "community detection in bipartite networks using weighted symmetric binary matrix factorization"
    - "maximizing barber s bipartite modularity is also hard"
    - "overlapping community detection in complex networks using symmetric binary matrix factorization"
    - "discovering link communities in complex networks by exploiting link dynamics"
    - "scalable spectral algorithms for community detection in directed networks"

Connected through hyperedge HE-8 (induced by source paper "common neighbours and the local community paradigm for topological link prediction in bipartite networks"):
  with members:
    - "evaluating link prediction methods"
    - "exploiting the structure of bipartite graphs for algebraic and spectral graph theory applications"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "the link prediction problem in bipartite 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.SI(Social and Information Networks)