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: "algebraic model counting"
  Abstract: Weighted model counting (WMC) is a well-known inference task on knowledge bases, used for probabilistic inference in graphical models. We introduce algebraic model counting (AMC), a generalization of WMC to a semiring structure. We show that AMC generalizes many well-known tasks in a variety of domains such as probabilistic inference, soft constraints and network and database analysis. Furthermore, we investigate AMC from a knowledge compilation perspective and show that all AMC tasks can be evaluated using sd-DNNF circuits. We identify further characteristics of AMC instances that allow for the use of even more succinct circuits.

Connected through hyperedge HE-1 (induced by source paper "semiring programming a declarative framework for generalized sum product problems"):
  with members:
    - "reasoning about discrete and continuous noisy sensors and effectors in dynamical systems"
    - "recursive decomposition for nonconvex optimization"
    - "the sum product theorem a foundation for learning tractable models"
    - "stable model counting and its application in probabilistic logic programming"
    - "optimization modulo theories with linear rational costs"
    - "on the complexity of optimization problems based on compiled nnf representations"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "inference in probabilistic logic programs using weighted cnf s"

Connected through hyperedge HE-2 (induced by source paper "smoothing structured decomposable circuits"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "on relaxing determinism in arithmetic circuits"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "maximum a posteriori inference in sum product networks"
    - "on the latent variable interpretation in sum product networks"
    - "the sum product theorem a foundation for learning tractable models"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "expander cnfs have exponential dnnf size"

Connected through hyperedge HE-3 (induced by source paper "does symbolic knowledge prevent adversarial fooling"):
  with members:
    - "neural markov logic networks"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "deepproblog neural probabilistic logic programming"
    - "intriguing properties of neural networks"
    - "on the robustness of most probable explanations"

Connected through hyperedge HE-4 (induced by source paper "scaling up probabilistic inference in linear and non linear hybrid domains by leveraging knowledge compilation"):
  with members:
    - "quantifying program bias"
    - "probabilistic inference modulo theories"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "symbolic dynamic programming for discrete and continuous state mdps"
    - "sum product networks a new deep architecture"
    - "solving sat and bayesian inference with backtracking search"
    - "a knowledge compilation map"

Connected through hyperedge HE-5 (induced by source paper "weighted model counting beyond two variable logic"):
  with members:
    - "on the uniform one dimensional fragment"
    - "new liftable classes for first order probabilistic inference"
    - "symmetric weighted first order model counting"
    - "regular graphs and the spectra of two variable logic with counting"
    - "skolemization for weighted first order model counting"

Connected through hyperedge HE-6 (induced by source paper "hunting for tractable languages for judgment aggregation"):
  with members:
    - "a partial taxonomy of judgment aggregation rules and their properties"
    - "backdoors to satisfaction"
    - "a knowledge compilation map"

Connected through hyperedge HE-7 (induced by source paper "knowledge compilation with continuous random variables and its application in hybrid probabilistic logic programming"):
  with members:
    - "a knowledge compilation map"

Connected through hyperedge HE-8 (induced by source paper "on relaxing determinism in arithmetic circuits"):
  with members:
    - "the sum product theorem a foundation for learning tractable models"
    - "a differential approach to inference in bayesian networks"
    - "learning arithmetic circuits"
    - "sum product networks a new deep architecture"
    - "a knowledge compilation map"

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)