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: "object centric behavioral constraints"
  Abstract: Today's process modeling languages often force the analyst or modeler to straightjacket real-life processes into simplistic or incomplete models that fail to capture the essential features of the domain under study. Conventional business process models only describe the lifecycles of individual instances (cases) in isolation. Although process models may include data elements (cf. BPMN), explicit connections to real data models (e.g., an entity relationship model or a UML class model) are rarely made. Therefore, we propose a novel approach that extends data models with a behavioral perspective. Data models can easily deal with many-to-many and one-to-many relationships. This is exploited to create process models that can also model complex interactions between different types of instances. Classical multiple-instance problems are circumvented by using the data model for event correlation. The declarative nature of the proposed language makes it possible to model behavioral constraints over activities like cardinality constraints in data models. The resulting object-centric behavioral constraint (OCBC) model is able to describe processes involving interacting instances and complex data dependencies. In this paper, we introduce the OCBC model and notation, providing a number of examples that give a flavour of the approach. We then define a set-theoretic semantics exploiting cardinality constraints within and across time points. We finally formalize conformance checking in our setting, arguing that evaluating conformance against OCBC models requires diagnostics that go beyond what is provided by contemporary conformance checking approaches.

Connected through hyperedge HE-1 (induced by source paper "extracting multiple viewpoint models from relational databases"):
  with members:
    - "process mining for python pm4py bridging the gap between process and data science"

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)