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: "memory enriched big bang big crunch optimization algorithm for data clustering"
  Abstract: Cluster analysis plays an important role in decision-making process for many knowledge-based systems. There exist a wide variety of different approaches for clustering applications including the heuristic techniques, probabilistic models, and traditional hierarchical algorithms. In this paper, a novel heuristic approach based on big bang–big crunch algorithm is proposed for clustering problems. The proposed method not only takes advantage of heuristic nature to alleviate typical clustering algorithms such as k-means, but it also benefits from the memory-based scheme as compared to its similar heuristic techniques. Furthermore, the performance of the proposed algorithm is investigated based on several benchmark test functions as well as on the well-known datasets. The experimental results show the significant superiority of the proposed method over the similar algorithms.

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"
    - "fasttext zip compressing text classification models"
    - "w2vlda almost unsupervised system for aspect based sentiment analysis"
    - "node2vec scalable feature learning for networks"
    - "relevant based structure learning for feature selection"
    - "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"
    - "fasttext zip compressing text classification models"
    - "w2vlda almost unsupervised system for aspect based sentiment analysis"
    - "node2vec scalable feature learning for networks"
    - "relevant based structure learning for feature selection"
    - "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 "iedc an integrated approach for overlapping and non overlapping community detection"):
  with members:
    - "relevant based structure learning for feature selection"
    - "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.AI(Artificial Intelligence)