human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "balancing scalability and uniformity in sat witness generator":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "quantitative verification of neural networks and its security applications")
  Member "a scalable approximate model counter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quantitative verification of neural networks and its security applications")
  Member "taming the curse of dimensionality discrete integration by hashing and optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards efficient discrete integration via adaptive quantile queries")
  Member "a scalable and nearly uniform generator of sat witnesses":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "constrained counting and sampling bridging the gap between theory and practice")
  Member "optimization with parity constraints from binary codes to discrete integration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "quantitative verification of neural networks and its security applications")
  Member "map complexity results and approximation methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a rate distortion framework for explaining neural network decisions")
  Member "probabilistic planning via heuristic forward search and weighted model counting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the approximability of weighted model integration on dnf structures")
  Member "complexity results and approximation strategies for map explanations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximate mmap by marginal search")

Question: Please tell me which class the source paper 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)