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: "comparative study for inference of hidden classes in stochastic block models"
  Abstract: Inference of hidden classes in stochastic block models is a classical problem with important applications. Most commonly used methods for this problem involve naive mean field approaches or heuristic spectral methods. Recently, belief propagation was proposed for this problem. In this contribution we perform a comparative study between the three methods on synthetically created networks. We show that belief propagation shows much better performance when compared to naive mean field and spectral approaches. This applies to accuracy, computational efficiency and the tendency to overfit the data.

Connected through hyperedge HE-1 (induced by source paper "exit analysis for community detection"):
  with members:
    - "recovering a single community with side information"
    - "community detection with side information exact recovery under the stochastic block model"
    - "thinking fast and slow with deep learning and tree search"
    - "the power of side information in subgraph detection"
    - "exact recovery in the stochastic block model"
    - "a tensor approach to learning mixed membership community models"

Connected through hyperedge HE-2 (induced by source paper "stochastic block models with multiple continuous attributes"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "structure and inference in annotated networks"
    - "clustering attributed graphs models measures and methods"
    - "link prediction in social networks the state of the art"
    - "producing a unified graph representation from multiple social network views"
    - "community detection in networks with node attributes"
    - "defining and evaluating network communities based on ground truth"
    - "empirical comparison of algorithms for network community detection"

Connected through hyperedge HE-3 (induced by source paper "community detection with side information exact recovery under the stochastic block model"):
  with members:
    - "the power of side information in subgraph detection"
    - "structure and inference in annotated networks"
    - "exact recovery in the stochastic block model"
    - "community detection via random and adaptive sampling"
    - "a tensor approach to learning mixed membership community models"
    - "community detection thresholds and the weak ramanujan property"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-4 (induced by source paper "recovering a single community with side information"):
  with members:
    - "community detection with side information exact recovery under the stochastic block model"
    - "the power of side information in subgraph detection"
    - "a tensor approach to learning mixed membership community models"

Connected through hyperedge HE-5 (induced by source paper "side information in the binary stochastic block model exact recovery"):
  with members:
    - "the power of side information in subgraph detection"
    - "structure and inference in annotated networks"
    - "exact recovery in the stochastic block model"
    - "community detection via random and adaptive sampling"
    - "a tensor approach to learning mixed membership community models"
    - "community detection thresholds and the weak ramanujan property"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-6 (induced by source paper "spectral redemption in clustering sparse networks"):
  with members:
    - "graph spectra and the detectability of community structure in networks"
    - "efficient and principled method for detecting communities in networks"
    - "a spectral approach to analysing belief propagation for 3 colouring"
    - "a tutorial on spectral clustering"
    - "a proof of alon s second eigenvalue conjecture and related problems"

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.LG(Machine Learning)