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: "uniform solution sampling using a constraint solver as an oracle"
  Abstract: We consider the problem of sampling from solutions defined by a set of hard constraints on a combinatorial space. We propose a new sampling technique that, while enforcing a uniform exploration of the search space, leverages the reasoning power of a systematic constraint solver in a black-box scheme. We present a series of challenging domains, such as energy barriers and highly asymmetric spaces, that reveal the difficulties introduced by hard constraints. We demonstrate that standard approaches such as Simulated Annealing and Gibbs Sampling are greatly affected, while our new technique can overcome many of these difficulties. Finally, we show that our sampling scheme naturally defines a new approximate model counting technique, which we empirically show to be very accurate on a range of benchmark problems.

Connected through hyperedge HE-1 (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"
    - "distribution aware sampling and weighted model counting for sat"
    - "probabilistic planning via heuristic forward search and weighted model counting"

Connected through hyperedge HE-2 (induced by source paper "faster creation of smaller test suites with snap"):
  with members:
    - "data driven search based software engineering"
    - "hyperparameter optimization for effort estimation"
    - "riot a stochastic based method for workflow scheduling in the cloud"
    - "revisiting unsupervised learning for defect prediction"
    - "beyond evolutionary algorithms for search based software engineering"
    - "a survey of symbolic execution techniques"
    - "sampling as a baseline optimizer for search based software engineering"
    - "enhancing reuse of constraint solutions to improve symbolic execution"

Connected through hyperedge HE-3 (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"
    - "a scalable and nearly uniform generator of sat witnesses"
    - "optimization with parity constraints from binary codes to discrete integration"

Connected through hyperedge HE-4 (induced by source paper "approximate model counting by partial knowledge compilation"):
  with members:
    - "reduced ordered binary decision diagram with implied literals a new knowledge compilation approach"
    - "and or multi valued decision diagrams aomdds for graphical models"
    - "the language of search"
    - "a knowledge compilation map"

Connected through hyperedge HE-5 (induced by source paper "adaptive fog configuration for the industrial internet of things"):
  with members:
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "a survey on mobile edge computing the communication perspective"
    - "request prediction in cloud with a cyclic window learning algorithm"

Connected through hyperedge HE-6 (induced by source paper "optimality clue for graph coloring problem"):
  with members:
    - "variations on memetic algorithms for graph coloring problems"

Connected through hyperedge HE-7 (induced by source paper "holoclean holistic data repairs with probabilistic inference"):
  with members:
    - "rapidly mixing gibbs sampling for a class of factor graphs using hierarchy width"
    - "truth finding on the deep web is the problem solved"
    - "slimfast guaranteed results for data fusion and source reliability"
    - "incremental knowledge base construction using deepdive"
    - "hinge loss markov random fields and probabilistic soft logic"
    - "dimmwitted a study of main memory statistical analytics"
    - "on the relative trust between inconsistent data and inaccurate constraints"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-8 (induced by source paper "sound probabilistic sat with projection"):
  with members:
    - "balancing scalability and uniformity in sat witness generator"
    - "sampling techniques for boolean satisfiability"
    - "a scalable approximate model counter"

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)