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: "deriving conclusions from non monotonic cause effect relations"
  Abstract: We present an extension of Logic Programming (under stable models semantics) that, not only allows concluding whether a true atom is a cause of another atom, but also deriving new conclusions from these causal-effect relations. This is expressive enough to capture informal rules like "if some agent's actions $\mathcal{A}$ have been necessary to cause an event $E$ then conclude atom $caused(\mathcal{A},E)$," something that, to the best of our knowledge, had not been formalised in the literature. To this aim, we start from a first attempt that proposed extending the syntax of logic programs with so-called causal literals. These causal literals are expressions that can be used in rule bodies and allow inspecting the derivation of some atom $A$ in the program with respect to some query function $\phi$. Depending on how these query functions are defined, we can model different types of causal relations such as sufficient, necessary or contributory causes, for instance. The initial approach was specifically focused on monotonic query functions. This was enough to cover sufficient cause-effect relations but, unfortunately, necessary and contributory are essentially non-monotonic. In this work, we define a semantics for non-monotonic causal literals showing that, not only extends the stable model semantics for normal logic programs, but also preserves many of its usual desirable properties for the extended syntax. Using this new semantics, we provide precise definitions of necessary and contributory causal relations and briefly explain their behaviour on a pair of typical examples from the Knowledge Representation literature. (Under consideration for publication in Theory and Practice of Logic Programming)

Connected through hyperedge HE-1 (induced by source paper "a rule based system for explainable donor patient matching in liver transplantation"):
  with members:
    - "answering the why in answer set programming a survey of explanation approaches"
    - "causal graph justifications of logic programs"
    - "functional answer set programming"
    - "justifications for logic programs under answer set semantics"

Connected through hyperedge HE-2 (induced by source paper "answering the why in answer set programming a survey of explanation approaches"):
  with members:
    - "achievements in answer set programming"
    - "enablers and inhibitors in causal justifications of logic programs"
    - "stepwise debugging of answer set programs"
    - "justifications for programs with disjunctive and causal choice rules"
    - "an integrated graphical user interface for debugging answer set programs"
    - "justifying answer sets using argumentation"
    - "asperix a first order forward chaining approach for answer set computing"
    - "generating explanations for biomedical queries"

Connected through hyperedge HE-3 (induced by source paper "justifications for programs with disjunctive and causal choice rules"):
  with members:
    - "enablers and inhibitors in causal justifications of logic programs"
    - "justifying answer sets using argumentation"
    - "causal graph justifications of logic programs"
    - "actual causation in cp logic"
    - "catching the ouroboros on debugging non ground answer set programs"
    - "justifications for logic programs under answer set semantics"
    - "stable models and an alternative logic programming paradigm"

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.LO(Logic in Computer Science)