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: "causes for query answers from databases datalog abduction view updates and integrity constraints"
  Abstract: Causality has been recently introduced in databases, to model, characterize, and possibly compute causes for query answers. Connections between QA-causality and consistency-based diagnosis and database repairs (wrt. integrity constraint violations) have already been established. In this work we establish precise connections between QA-causality and both abductive diagnosis and the view-update problem in databases, allowing us to obtain new algorithmic and complexity results for QA-causality. We also obtain new results on the complexity of view-conditioned causality, and investigate the notion of QA-causality in the presence of integrity constraints, obtaining complexity results from a connection with view-conditioned causality. The abduction connection under integrity constraints allows us to obtain algorithmic tools for QA-causality.

Connected through hyperedge HE-1 (induced by source paper "data management for causal algorithmic fairness"):
  with members:
    - "capuchin causal database repair for algorithmic fairness"
    - "causal reasoning for algorithmic fairness"
    - "bias in olap queries detection explanation and removal"
    - "fairness testing testing software for discrimination"
    - "snorkel rapid training data creation with weak supervision"
    - "computing optimal repairs for functional dependencies"
    - "algorithmic decision making and the cost of fairness"
    - "quantifying causal effects on query answering in databases"

Connected through hyperedge HE-2 (induced by source paper "example driven query intent discovery abductive reasoning using semantic similarity"):
  with members:
    - "learning from positive and unlabeled data a survey"
    - "the complexity of reverse engineering problems for conjunctive queries"
    - "compositional vector space models for knowledge base completion"
    - "querying knowledge graphs by example entity tuples"
    - "learning and verifying quantified boolean queries by example"
    - "coherent integration of databases by abductive logic programming"

Connected through hyperedge HE-3 (induced by source paper "the impact of negation on the complexity of the shapley value in conjunctive queries"):
  with members:
    - "the shapley value of tuples in query answering"
    - "model counting for conjunctive queries without self joins"
    - "a unified approach to interpreting model predictions"
    - "quantifying causal effects on query answering in databases"
    - "the complexity of causality and responsibility for query answers and non answers"

Connected through hyperedge HE-4 (induced by source paper "the shapley value of tuples in query answering"):
  with members:
    - "quantifying causal effects on query answering in databases"
    - "shapley meets shapley"
    - "the complexity of causality and responsibility for query answers and non answers"
    - "why so or why no functional causality for explaining query answers"

Connected through hyperedge HE-5 (induced by source paper "the causality repair connection in databases causality programs"):
  with members:
    - "consistency and trust in peer data exchange systems"
    - "quantifying causal effects on query answering in databases"
    - "achieving data privacy through secrecy views and null based virtual updates"
    - "the complexity of causality and responsibility for query answers and non answers"
    - "design and implementation of aggregate functions in the dlv system"
    - "the dlv system for knowledge representation and reasoning"
    - "responsibility and blame a structural model approach"
    - "answer sets for consistent query answering in inconsistent databases"

Connected through hyperedge HE-6 (induced by source paper "a framework for inferring causality from multi relational observational data using conditional independence"):
  with members:
    - "a sound and complete algorithm for learning causal models from relational data"

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)