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: "computing optimal repairs for functional dependencies"
  Abstract: We investigate the complexity of computing an optimal repair of an inconsistent database, in the case where integrity constraints are Functional Dependencies (FDs). We focus on two types of repairs: an optimal subset repair (optimal S-repair) that is obtained by a minimum number of tuple deletions, and an optimal update repair (optimal U-repair) that is obtained by a minimum number of value (cell) updates. For computing an optimal S-repair, we present a polynomial-time algorithm that succeeds on certain sets of FDs and fails on others. We prove the following about the algorithm. When it succeeds, it can also incorporate weighted tuples and duplicate tuples. When it fails, the problem is NP-hard, and in fact, APX-complete (hence, cannot be approximated better than some constant). Thus, we establish a dichotomy in the complexity of computing an optimal S-repair. We present general analysis techniques for the complexity of computing an optimal U-repair, some based on the dichotomy for S-repairs. We also draw a connection to a past dichotomy in the complexity of finding a "most probable database" that satisfies a set of FDs with a single attribute on the left hand side; the case of general FDs was left open, and we show how our dichotomy provides the missing generalization and thereby settles the open problem.

Connected through hyperedge HE-1 (induced by source paper "complexity and efficient algorithms for data inconsistency evaluating and repairing"):
  with members:
    - "a formal framework for probabilistic unclean databases"
    - "on the relative trust between inconsistent data and inaccurate constraints"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"
    - "minimal change integrity maintenance using tuple deletions"

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"
    - "algorithmic decision making and the cost of fairness"
    - "quantifying causal effects on query answering in databases"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-3 (induced by source paper "capuchin causal database repair for algorithmic fairness"):
  with members:
    - "causal reasoning for algorithmic fairness"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "bias in olap queries detection explanation and removal"
    - "fairness testing testing software for discrimination"
    - "learning non discriminatory predictors"
    - "algorithmic decision making and the cost of fairness"
    - "equality of opportunity in supervised learning"
    - "fairtest discovering unwarranted associations in data driven applications"

Connected through hyperedge HE-4 (induced by source paper "principles of progress indicators for database repairing"):
  with members:
    - "measuring and computing database inconsistency via repairs"
    - "research directions for principles of data management dagstuhl perspectives workshop 16151"
    - "holoclean holistic data repairs with probabilistic inference"
    - "matching dependencies with arbitrary attribute values semantics query answering and integrity constraints"
    - "complexity of consistent query answering in databases under cardinality based and incremental repair semantics"

Connected through hyperedge HE-5 (induced by source paper "generalized deletion propagation on counting conjunctive query answers"):
  with members:
    - "size bounds and query plans for relational joins"
    - "the complexity of causality and responsibility for query answers and non answers"

Connected through hyperedge HE-6 (induced by source paper "repair based degrees of database inconsistency computation and complexity"):
  with members:
    - "consistency and trust in peer data exchange systems"
    - "juggling functions inside a database"
    - "achieving data privacy through secrecy views and null based virtual updates"
    - "the complexity of causality and responsibility for query answers and non answers"
    - "prioritized repairing and consistent query answering in relational databases"
    - "the dlv system for knowledge representation and reasoning"
    - "complexity of consistent query answering in databases under cardinality based and incremental repair semantics"
    - "minimal change integrity maintenance using tuple deletions"

Connected through hyperedge HE-7 (induced by source paper "a formal framework for probabilistic unclean databases"):
  with members:
    - "the complexity of computing a cardinality repair for functional dependencies"
    - "holoclean holistic data repairs with probabilistic inference"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "complexity of consistent query answering in databases under cardinality based and incremental repair semantics"
    - "minimal change integrity maintenance using tuple deletions"

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)