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: "distribution aware sampling and weighted model counting for sat"
  Abstract: Given a CNF formula and a weight for each assignment of values to variables, two natural problems are weighted model counting and distribution-aware sampling of satisfying assignments. Both problems have a wide variety of important applications. Due to the inherent complexity of the exact versions of the problems, interest has focused on solving them approximately. Prior work in this area scaled only to small problems in practice, or failed to provide strong theoretical guarantees, or employed a computationally-expensive maximum a posteriori probability (MAP) oracle that assumes prior knowledge of a factored representation of the weight distribution. We present a novel approach that works with a black-box oracle for weights of assignments and requires only an {\NP}-oracle (in practice, a SAT-solver) to solve both the counting and sampling problems. Our approach works under mild assumptions on the distribution of weights of satisfying assignments, provides strong theoretical guarantees, and scales to problems involving several thousand variables. We also show that the assumptions can be significantly relaxed while improving computational efficiency if a factored representation of the weights is known.

Connected through hyperedge HE-1 (induced by source paper "on the approximability of weighted model integration on dnf structures"):
  with members:
    - "learning to reason leveraging neural networks for approximate dnf counting"
    - "on hashing based approaches to approximate dnf counting"
    - "geodesic walks in polytopes"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
    - "probabilistic theorem proving"
    - "approximating the volume of unions and intersections of high dimensional geometric objects"

Connected through hyperedge HE-2 (induced by source paper "exploiting database management systems and treewidth for counting"):
  with members:
    - "exploiting treewidth for projected model counting and its limits"
    - "experimental evaluation of parameterized algorithms for feedback vertex set"
    - "practical access to dynamic programming on tree decompositions"
    - "implementing efficient all solutions sat solvers"
    - "uniform solution sampling using a constraint solver as an oracle"
    - "probabilistic planning via heuristic forward search and weighted model counting"

Connected through hyperedge HE-3 (induced by source paper "towards efficient discrete integration via adaptive quantile queries"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "probabilistic model counting with short xors"
    - "stable model counting and its application in probabilistic logic programming"
    - "loopy belief propagation for approximate inference an empirical study"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "on the partition function and random maximum a posteriori perturbations"

Connected through hyperedge HE-4 (induced by source paper "quantitative verification of neural networks and its security applications"):
  with members:
    - "phase transition behavior of cardinality and xor constraints"
    - "adversarial examples are a natural consequence of test error in noise"
    - "strip a defence against trojan attacks on deep neural networks"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "adversarial spheres"

Connected through hyperedge HE-5 (induced by source paper "phase transition behavior of cardinality and xor constraints"):
  with members:
    - "combining the k cnf and xor phase transitions"
    - "the hard problems are almost everywhere for random cnf xor formulas"
    - "property driven fence insertion using reorder bounded model checking"
    - "a scalable approximate model counter"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "list decoding of polar codes"
    - "probabilistic planning via heuristic forward search and weighted model counting"

Connected through hyperedge HE-6 (induced by source paper "adawish faster discrete integration via adaptive quantiles"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "probabilistic model counting with short xors"
    - "closing the gap between short and long xors for model counting"
    - "stable model counting and its application in probabilistic logic programming"
    - "loopy belief propagation for approximate inference an empirical study"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "on the partition function and random maximum a posteriori perturbations"

Connected through hyperedge HE-7 (induced by source paper "predicting performance of software configurations there is no silver bullet"):
  with members:
    - "finding faster configurations using flash"
    - "using bad learners to find good configurations"
    - "easy over hard a case study on deep learning"
    - "faster discovery of faster system configurations with spectral learning"
    - "a comparison of 10 sampling algorithms for configurable systems"
    - "tuning for software analytics"

Connected through hyperedge HE-8 (induced by source paper "efficient search based weighted model integration"):
  with members:
    - "probabilistic inference modulo theories"
    - "approximate probabilistic inference via word level counting"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "solving sat and bayesian inference with backtracking search"

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.AI(Artificial Intelligence)