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: "definable inapproximability new challenges for duplicator"
  Abstract: We consider the hardness of approximation of optimization problems from the point of view of definability. For many NP-hard optimization problems it is known that, unless P = NP, no polynomial-time algorithm can give an approximate solution guaranteed to be within a fixed constant factor of the optimum. We show, in several such instances and without any complexity theoretic assumption, that no algorithm that is expressible in fixed-point logic with counting (FPC) can compute an approximate solution. Since important algorithmic techniques for approximation algorithms (such as linear or semidefinite programming) are expressible in FPC, this yields lower bounds on what can be achieved by such methods. The results are established by showing lower bounds on the number of variables required in first-order logic with counting to separate instances with a high optimum from those with a low optimum for fixed-size instances.

Connected through hyperedge HE-1 (induced by source paper "on the weisfeiler leman dimension of fractional packing"):
  with members:
    - "the power of the weisfeiler leman algorithm to decompose graphs"
    - "a survey on graph kernels"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "graph isomorphism in quasipolynomial time"
    - "dimension reduction via colour refinement"

Connected through hyperedge HE-2 (induced by source paper "on the power of symmetric linear programs"):
  with members:
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "on symmetric circuits and fixed point logics"
    - "approximate constraint satisfaction requires large lp relaxations"
    - "the matching polytope has exponential extension complexity"

Connected through hyperedge HE-3 (induced by source paper "constructing hard examples for graph isomorphism"):
  with members:
    - "an exponential lower bound for individualization refinement algorithms for graph isomorphism"
    - "the pebbling comonad in finite model theory"
    - "benchmark graphs for practical graph isomorphism"
    - "graph isomorphism in quasipolynomial time"
    - "lower bounds for existential pebble games and k consistency tests"
    - "practical graph isomorphism ii"
    - "clause learning algorithms with many restarts and bounded width resolution"

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)