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: "the complexity of causality and responsibility for query answers and non answers"
  Abstract: An answer to a query has a well-defined lineage expression (alternatively called how-provenance) that explains how the answer was derived. Recent work has also shown how to compute the lineage of a non-answer to a query. However, the cause of an answer or non-answer is a more subtle notion and consists, in general, of only a fragment of the lineage. In this paper, we adapt Halpern, Pearl, and Chockler's recent definitions of causality and responsibility to define the causes of answers and non-answers to queries, and their degree of responsibility. Responsibility captures the notion of degree of causality and serves to rank potentially many causes by their relative contributions to the effect. Then, we study the complexity of computing causes and responsibilities for conjunctive queries. It is known that computing causes is NP-complete in general. Our first main result shows that all causes to conjunctive queries can be computed by a relational query which may involve negation. Thus, causality can be computed in PTIME, and very efficiently so. Next, we study computing responsibility. Here, we prove that the complexity depends on the conjunctive query and demonstrate a dichotomy between PTIME and NP-complete cases. For the PTIME cases, we give a non-trivial algorithm, consisting of a reduction to the max-flow computation problem. Finally, we prove that, even when it is in PTIME, responsibility is complete for LOGSPACE, implying that, unlike causality, it cannot be computed by a relational query.

Connected through hyperedge HE-1 (induced by source paper "causality based explanation of classification outcomes"):
  with members:
    - "the shapley value of tuples in query answering"
    - "explaining classifiers with causal concept effect cace"
    - "explainable ai for trees from local explanations to global understanding"
    - "an interpretable model with globally consistent explanations for credit risk"
    - "a unified approach to interpreting model predictions"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "responsibility and blame a structural model approach"

Connected through hyperedge HE-2 (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-3 (induced by source paper "efficiently checking actual causality with sat solving"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "towards formal definitions of blameworthiness intention and moral responsibility"
    - "understanding and formalizing accountability for cyber physical systems"
    - "from causes for database queries to repairs and model based diagnosis and back"
    - "a modification of the halpern pearl definition of causality"
    - "the computational complexity of structure based causality"
    - "what causes a system to satisfy a specification"
    - "responsibility and blame a structural model approach"

Connected through hyperedge HE-4 (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"
    - "causes for query answers from databases datalog abduction view updates and integrity constraints"

Connected through hyperedge HE-5 (induced by source paper "the shapley value of tuples in query answering"):
  with members:
    - "quantifying causal effects on query answering in databases"
    - "causes for query answers from databases datalog abduction view updates and integrity constraints"
    - "shapley meets shapley"
    - "why so or why no functional causality for explaining query answers"

Connected through hyperedge HE-6 (induced by source paper "explain3d explaining disagreements in disjoint datasets"):
  with members:
    - "high level why not explanations using ontologies"
    - "recent advances in graph partitioning"
    - "perfxplain debugging mapreduce job performance"
    - "explanation based auditing"

Connected through hyperedge HE-7 (induced by source paper "new results for the complexity of resilience for binary conjunctive queries with self joins"):
  with members:
    - "consistent query answering for primary keys in logspace"
    - "conjunctive queries on probabilistic graphs combined complexity"
    - "a dichotomy on the complexity of consistent query answering for atoms with simple keys"

Connected through hyperedge HE-8 (induced by source paper "generalized deletion propagation on counting conjunctive query answers"):
  with members:
    - "size bounds and query plans for relational joins"
    - "computing optimal repairs for functional dependencies"

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)