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: "exploiting answer set programming with external sources for meta interpretive learning"
  Abstract: Meta-Interpretive Learning (MIL) learns logic programs from examples by instantiating meta-rules, which is implemented by the Metagol system based on Prolog. Viewing MIL-problems as combinatorial search problems, they can alternatively be solved by employing Answer Set Programming (ASP), which may result in performance gains as a result of efficient conflict propagation. However, a straightforward ASP-encoding of MIL results in a huge search space due to a lack of procedural bias and the need for grounding. To address these challenging issues, we encode MIL in the HEX-formalism, which is an extension of ASP that allows us to outsource the background knowledge, and we restrict the search space to compensate for a procedural bias in ASP. This way, the import of constants from the background knowledge can for a given type of meta-rules be limited to relevant ones. Moreover, by abstracting from term manipulations in the encoding and by exploiting the HEX interface mechanism, the import of such constants can be entirely avoided in order to mitigate the grounding bottleneck. An experimental evaluation shows promising results.

Connected through hyperedge HE-1 (induced by source paper "inductive general game playing"):
  with members:
    - "inductive learning of answer set programs from noisy examples"
    - "learning explanatory rules from noisy data"
    - "learning first order definable concepts over structures of small degree"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "iterative learning of answer set programs from context dependent examples"
    - "structure learning of probabilistic logic programs by searching the clause space"
    - "learning weak constraints in answer set programming"
    - "clingo asp control preliminary report"

Connected through hyperedge HE-2 (induced by source paper "sld resolution reduction of second order horn fragments technical report"):
  with members:
    - "logical rule induction and theory learning using neural theorem proving"
    - "learning explanatory rules from noisy data"
    - "end to end differentiable proving"
    - "deep learning for ontology reasoning"
    - "minimization for generalized boolean formulas"
    - "redundancy deduction schemes and minimum size bases for association rules"
    - "redundancy deduction schemes and minimum size bases for association rules"
    - "redundancy in logic i cnf propositional formulae"

Connected through hyperedge HE-3 (induced by source paper "logical reduction of metarules"):
  with members:
    - "inductive general game playing"
    - "logical rule induction and theory learning using neural theorem proving"
    - "learning explanatory rules from noisy data"
    - "minimization for generalized boolean formulas"
    - "redundancy in logic i cnf propositional formulae"
    - "redundancy in logic ii 2cnf and horn propositional formulae"

Connected through hyperedge HE-4 (induced by source paper "playgol learning programs through play"):
  with members:
    - "learning explanatory rules from noisy data"
    - "learning to infer graphics programs from hand drawn images"
    - "deepcoder learning to write programs"
    - "learning to learn"

Connected through hyperedge HE-5 (induced by source paper "learning higher order logic programs"):
  with members:
    - "a model building framework for answer set programming with external computations"

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)