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: "multi dimensional boltzmann sampling of languages"
  Abstract: This paper addresses the uniform random generation of words from a context-free language (over an alphabet of size $k$), while constraining every letter to a targeted frequency of occurrence. Our approach consists in a multidimensional extension of Boltzmann samplers \cite{Duchon2004}. We show that, under mostly \emph{strong-connectivity} hypotheses, our samplers return a word of size in $[(1-\varepsilon)n, (1+\varepsilon)n]$ and exact frequency in $\mathcal{O}(n^{1+k/2})$ expected time. Moreover, if we accept tolerance intervals of width in $\Omega(\sqrt{n})$ for the number of occurrences of each letters, our samplers perform an approximate-size generation of words in expected $\mathcal{O}(n)$ time. We illustrate these techniques on the generation of Tetris tessellations with uniform statistics in the different types of tetraminoes.

Connected through hyperedge HE-1 (induced by source paper "efficient random sampling of binary and unary binary trees via holonomic equations"):
  with members:
    - "random bit optimal uniform sampling for rooted planar trees with given sequence of degrees and applications"
    - "random generation of combinatorial structures boltzmann samplers and beyond"

Connected through hyperedge HE-2 (induced by source paper "random bit optimal uniform sampling for rooted planar trees with given sequence of degrees and applications"):
  with members:
    - "controlled non uniform random generation of decomposable structures"

Connected through hyperedge HE-3 (induced by source paper "asymptotic analysis and random sampling of digitally convex polyominoes"):
  with members:
    - "boltzmann samplers for colored combinatorial objects"

Connected through hyperedge HE-4 (induced by source paper "analytic samplers and the combinatorial rejection method"):
  with members:
    - "optimal discrete uniform generation from coin flips and applications"

Connected through hyperedge HE-5 (induced by source paper "non redundant random generation algorithms for weighted context free languages"):
  with members:
    - "controlled non uniform random generation of decomposable structures"
    - "on the average complexity of moore s state minimization algorithm"
    - "uniform random sampling of traces in very large models"

Connected through hyperedge HE-6 (induced by source paper "the weighted words collector"):
  with members:
    - "controlled non uniform random generation of decomposable structures"
    - "weighted random generation of context free languages analysis of collisions in random urn occupancy models"

Connected through hyperedge HE-7 (induced by source paper "rule weighted and terminal weighted context free grammars have identical expressivity"):
  with members:
    - "controlled non uniform random generation of decomposable structures"

Connected through hyperedge HE-8 (induced by source paper "weighted random generation of context free languages analysis of collisions in random urn occupancy models"):
  with members:
    - "non redundant random generation from weighted context free languages"
    - "on the average complexity of moore s state minimization algorithm"
    - "uniform random sampling of traces in very large models"

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.DS(Data Structures and Algorithms)