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: "a scalable and nearly uniform generator of sat witnesses"
  Abstract: Functional verification constitutes one of the most challenging tasks in the development of modern hardware systems, and simulation-based verification techniques dominate the functional verification landscape. A dominant paradigm in simulation-based verification is directed random testing, where a model of the system is simulated with a set of random test stimuli that are uniformly or near-uniformly distributed over the space of all stimuli satisfying a given set of constraints. Uniform or near-uniform generation of solutions for large constraint sets is therefore a problem of theoretical and practical interest. For Boolean constraints, prior work offered heuristic approaches with no guarantee of performance, and theoretical approaches with proven guarantees, but poor performance in practice. We offer here a new approach with theoretical performance guarantees and demonstrate its practical utility on large constraint sets.

Connected through hyperedge HE-1 (induced by source paper "constrained counting and sampling bridging the gap between theory and practice"):
  with members:
    - "approximate probabilistic inference via word level counting"
    - "distribution aware sampling and weighted model counting for sat"
    - "balancing scalability and uniformity in sat witness generator"
    - "structured learning modulo theories"
    - "a scalable approximate model counter"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "optimization with parity constraints from binary codes to discrete integration"
    - "automated feedback generation for introductory programming assignments"

Connected through hyperedge HE-2 (induced by source paper "combining the k cnf and xor phase transitions"):
  with members:
    - "constrained sampling and counting universal hashing meets sat solving"
    - "aspects of statistical physics in computational complexity"
    - "distribution aware sampling and weighted model counting for sat"
    - "balancing scalability and uniformity in sat witness generator"
    - "a scalable approximate model counter"
    - "on the concentration of the number of solutions of random satisfiability formulas"

Connected through hyperedge HE-3 (induced by source paper "flexible constrained sampling with guarantees for pattern mining"):
  with members:
    - "constrained sampling and counting universal hashing meets sat solving"
    - "distribution aware sampling and weighted model counting for sat"
    - "abacus frequent pattern mining based community discovery in multidimensional networks"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"

Connected through hyperedge HE-4 (induced by source paper "probabilistic model counting with short xors"):
  with members:
    - "distribution aware sampling and weighted model counting for sat"
    - "balancing scalability and uniformity in sat witness generator"
    - "a scalable approximate model counter"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"

Connected through hyperedge HE-5 (induced by source paper "xor sampling for network design with correlated stochastic events"):
  with members:
    - "solving marginal map problems with np oracles and parity constraints"
    - "maximizing the spread of cascades using network design"
    - "improving the accuracy and efficiency of map inference for markov logic"

Connected through hyperedge HE-6 (induced by source paper "the hard problems are almost everywhere for random cnf xor formulas"):
  with members:
    - "constrained sampling and counting universal hashing meets sat solving"
    - "distribution aware sampling and weighted model counting for sat"
    - "threshold values of random k sat from the cavity method"

Connected through hyperedge HE-7 (induced by source paper "constrained sampling and counting universal hashing meets sat solving"):
  with members:
    - "distribution aware sampling and weighted model counting for sat"
    - "balancing scalability and uniformity in sat witness generator"
    - "a scalable approximate model counter"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "optimization with parity constraints from binary codes to discrete integration"
    - "uniform solution sampling using a constraint solver as an oracle"
    - "complexity results and approximation strategies for map explanations"
    - "the good old davis putnam procedure helps counting models"

Connected through hyperedge HE-8 (induced by source paper "closing the gap between short and long xors for model counting"):
  with members:
    - "a scalable approximate model counter"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "optimization with parity constraints from binary codes to discrete integration"
    - "probabilistic theorem proving"

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)