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: "mackey complete spaces and power series a topological model of differential linear logic"
  Abstract: In this paper, we have described a denotational model of Intuitionist Linear Logic which is also a differential category. Formulas are interpreted as Mackey-complete topological vector space and linear proofs are interpreted by bounded linear functions. So as to interpret non-linear proofs of Linear Logic, we have used a notion of power series between Mackey-complete spaces, generalizing the notion of entire functions in C. Finally, we have obtained a quantitative model of Intuitionist Differential Linear Logic, where the syntactic differentiation correspond to the usual one and where the interpretations of proofs satisfy a Taylor expansion decomposition.

Connected through hyperedge HE-1 (induced by source paper "change actions from incremental computation to discrete derivatives"):
  with members:
    - "the differential calculus of causal functions"
    - "change actions models of generalised differentiation"
    - "differentiable causal computations via delayed trace"
    - "evolving the incremental λ calculus into a model of forward automatic differentiation ad"
    - "rex recursive delta based data centric computation"
    - "a convenient differential category"
    - "a computational definition of the notion of vectorial space"

Connected through hyperedge HE-2 (induced by source paper "jets and differential linear logic"):
  with members:
    - "reverse derivative categories"
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "efficient neural architecture search via parameter sharing"
    - "cofree coalgebras and differential linear logic"
    - "an introduction to differential linear logic proof nets models and antiderivatives"
    - "neural architecture search with reinforcement learning"
    - "a convenient differential category"

Connected through hyperedge HE-3 (induced by source paper "fixing incremental computation derivatives of fixpoints and the recursive semantics of datalog"):
  with members:
    - "change actions models of generalised differentiation"
    - "the simple essence of automatic differentiation"
    - "an introduction to differential linear logic proof nets models and antiderivatives"
    - "diffsharp an ad library for net languages"
    - "automatic differentiation of algorithms for machine learning"

Connected through hyperedge HE-4 (induced by source paper "weak topologies for linear logic"):
  with members:
    - "interaction graphs multiplicatives"

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)