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: "on the formation of circles in co authorship networks"
  Abstract: The availability of an overwhelmingly large amount of bibliographic information including citation and co-authorship data makes it imperative to have a systematic approach that will enable an author to organize her own personal academic network profitably. An effective method could be to have one's co-authorship network arranged into a set of "circles", which has been a recent practice for organizing relationships (e.g., friendship) in many online social networks. #R##N#In this paper, we propose an unsupervised approach to automatically detect circles in an ego network such that each circle represents a densely knit community of researchers. Our model is an unsupervised method which combines a variety of node features and node similarity measures. The model is built from a rich co-authorship network data of more than 8 hundred thousand authors. In the first level of evaluation, our model achieves 13.33% improvement in terms of overlapping modularity compared to the best among four state-of-the-art community detection methods. Further, we conduct a task-based evaluation -- two basic frameworks for collaboration prediction are considered with the circle information (obtained from our model) included in the feature set. Experimental results show that including the circle information detected by our model improves the prediction performance by 9.87% and 15.25% on average in terms of AUC (Area under the ROC) and P rec@20 (Precision at Top 20) respectively compared to the case, where the circle information is not present.

Connected through hyperedge HE-1 (induced by source paper "ensemble based overlapping community detection using disjoint community structures"):
  with members:
    - "dyperm maximizing permanence for dynamic community detection"
    - "iedc an integrated approach for overlapping and non overlapping community detection"
    - "ensemble based algorithms to detect disjoint and overlapping communities in networks"
    - "metrics for community analysis a survey"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "leveraging disjoint communities for detecting overlapping community structure"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "discovering social circles in ego networks"

Connected through hyperedge HE-2 (induced by source paper "cone community oriented network embedding"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "sequence to sequence learning with neural networks"
    - "distributed representations of words and phrases and their compositionality"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-3 (induced by source paper "learning social circles in ego networks based on multi view social graphs"):
  with members:
    - "clustering on multiple incomplete datasets via collective kernel learning"
    - "community detection in networks with node attributes"
    - "discovering social circles in ego networks"
    - "clustering partially observed graphs via convex optimization"
    - "fast and accurate annotation of short texts with wikipedia pages"

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)