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: "active community detection a maximum likelihood approach"
  Abstract: We propose novel semi-supervised and active learning algorithms for the problem of community detection on networks. The algorithms are based on optimizing the likelihood function of the community assignments given a graph and an estimate of the statistical model that generated it. The optimization framework is inspired by prior work on the unsupervised community detection problem in Stochastic Block Models (SBM) using Semi-Definite Programming (SDP). In this paper we provide the next steps in the evolution of learning communities in this context which involves a constrained semi-definite programming algorithm, and a newly presented active learning algorithm. The active learner intelligently queries nodes that are expected to maximize the change in the model likelihood. Experimental results show that this active learning algorithm outperforms the random-selection semi-supervised version of the same algorithm as well as other state-of-the-art active learning algorithms. Our algorithms significantly improved performance is demonstrated on both real-world and SBM-generated networks even when the SBM has a signal to noise ratio (SNR) below the known unsupervised detectability threshold.

Connected through hyperedge HE-1 (induced by source paper "renyi entropy bounds on the active learning cost performance tradeoff"):
  with members:
    - "graph powering and spectral robustness"
    - "a survey on theoretical advances of community detection in networks"
    - "connectivity in random annulus graphs and the geometric block model"
    - "active learning for community detection in stochastic block models"
    - "s2 an efficient graph based active learning algorithm with application to nonparametric classification"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "exact recovery in the stochastic block model"
    - "active learning on trees and graphs"

Connected through hyperedge HE-2 (induced by source paper "characterization of citizens using word2vec and latent topic analysis in a large set of tweets"):
  with members:
    - "exploiting deep learning for persian sentiment analysis"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "different approaches to community detection"
    - "fasttext zip compressing text classification models"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "notes on noise contrastive estimation and negative sampling"

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)