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: "relevant based structure learning for feature selection"
  Abstract: Abstract   Feature selection is an important task in many problems occurring in pattern recognition, bioinformatics, machine learning and data mining applications. The feature selection approach enables us to reduce the computation burden and the falling accuracy effect of dealing with huge number of features in typical learning problems. There is a variety of techniques for feature selection in supervised learning problems based on different selection metrics. In this paper, we propose a novel unified framework for feature selection built on the graphical models and information theoretic tools. The proposed approach exploits the structure learning among features to select more relevant and less redundant features to the predictive modeling problem according to a primary novel likelihood based criterion. In line with the selection of the optimal subset of features through the proposed method, it provides us the Bayesian network classifier without the additional cost of model training on the selected subset of features. The optimal properties of our method are established through empirical studies and computational complexity analysis. Furthermore the proposed approach is evaluated on a bunch of benchmark datasets based on the well-known classification algorithms. Extensive experiments confirm the significant improvement of the proposed approach compared to the earlier works.

Connected through hyperedge HE-1 (induced by source paper "leveraging deep graph based text representation for sentiment polarity applications"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "memory enriched big bang big crunch optimization algorithm for data clustering"
    - "fasttext zip compressing text classification models"
    - "w2vlda almost unsupervised system for aspect based sentiment analysis"
    - "node2vec scalable feature learning for networks"
    - "deep learning in neural networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "deep sentiment analysis using a graph based text representation"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "memory enriched big bang big crunch optimization algorithm for data clustering"
    - "fasttext zip compressing text classification models"
    - "w2vlda almost unsupervised system for aspect based sentiment analysis"
    - "node2vec scalable feature learning for networks"
    - "deep learning in neural networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "unsupervised feature selection based on adaptive similarity learning and subspace clustering"):
  with members:
    - "feature selection a data perspective"
    - "unsupervised feature selection with adaptive structure learning"
    - "robust subspace clustering"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-4 (induced by source paper "iedc an integrated approach for overlapping and non overlapping community detection"):
  with members:
    - "memory enriched big bang big crunch optimization algorithm for data clustering"
    - "uncovering the small community structure in large networks a local spectral approach"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "general optimization technique for high quality community detection in complex networks"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "social structure of facebook networks"
    - "towards linear time overlapping community detection in social networks"
    - "defining and evaluating network communities based on ground truth"

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)