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: "generalized graph pattern matching"
  Abstract: Most of the machine learning algorithms are limited to learn from flat data: a recordset with prefixed structure. When learning from a record, these types of algorithms don't take into account other objects even though they are directly connected to it and can provide valuable information for the learning task. In this paper we present the concept of Generalized Graph Query, a query tool over graphs or multi-relational data structures. They are built using the same graph structure as generalized graphs and allow to express powerful relational and non-relational restrictions on this type of data. Also, this paper shows mechanisms to build this kind of queries dynamically and how they can be used to perform bottom-up discovery processes through machine laerning techniques. #R##N#----- #R##N#La mayor\'ia de los algoritmos que aprenden a partir de datos est\'an limitados ya que s\'olo son capaces de aprender a partir de datos estructurados en forma de tabla en la que cada fila representa un registro y cada columna una propiedad asociada. Estos algoritmos, no tienen en cuenta los atributos de las estructuras con las que un registro dado puede estar relacionado, a pesar de que \'estos pueden aportar informaci\'on \'util a la hora de llevar a cabo la tarea de aprendizaje. En este art\'iculo presentamos el concepto de Generalized Graph Query, una herramienta de consulta de patrones en grafos generalizados. Dicha herramienta ha sido construida utilizando la estructura de Grafo Generalizado y permite expresar restricciones relacionales y no relacionales sobre este tipo de estructuras. Adem\'as, en este art\'iculo se presentan mecanismos para la construcci\'on autom\'atica de este tipo de consultas y se muestra c\'omo \'estas pueden ser utilizadas en procesos de descubrimiento tipo bottom-up a trav\'es de t\'ecnicas relacionadas con el Aprendizaje Autom\'atico.

Connected through hyperedge HE-1 (induced by source paper "semantic preserving embeddings for generalized graphs"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "enriching word vectors with subword information"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "efficient estimation of word representations in vector space"

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.DB(Databases)