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: "programming discrete distributions with chemical reaction networks"
  Abstract: We explore the range of probabilistic behaviours that can be engineered with Chemical Reaction Networks (CRNs). We show that at steady state CRNs are able to "program" any distribution with finite support in $\mathbb{N}^m$, with $m \geq 1$. Moreover, any distribution with countable infinite support can be approximated with arbitrarily small error under the $L^1$ norm. We also give optimized schemes for special distributions, including the uniform distribution. Finally, we formulate a calculus to compute on distributions that is complete for finite support distributions, and can be compiled to a restricted class of CRNs that at steady state realize those distributions.

Connected through hyperedge HE-1 (induced by source paper "computing with chemical reaction networks a tutorial"):
  with members:
    - "hardness of computing and approximating predicates and functions with leaderless population protocols"
    - "verifying chemical reaction network implementations a pathway decomposition approach"
    - "democratic existential and consensus based output conventions in stable computation by chemical reaction networks"
    - "reachability problems for continuous chemical reaction networks"
    - "stable leader election in population protocols requires linear time"
    - "dominance and deficiency for petri nets and chemical reaction networks"
    - "minimal output unstable configurations in chemical reaction networks and deciders"
    - "leaderless deterministic chemical reaction networks"

Connected through hyperedge HE-2 (induced by source paper "central limit model checking"):
  with members:
    - "probabilistic model checking for continuous time markov chains via sequential bayesian inference"
    - "efficient checking of individual rewards properties in markov population models"
    - "smoothed model checking for uncertain continuous time markov chains"
    - "fluid model checking"
    - "hybrid behaviour of markov population models"

Connected through hyperedge HE-3 (induced by source paper "stochastic chemical reaction networks for robustly approximating arbitrary probability distributions"):
  with members:
    - "a reaction network scheme which implements the em algorithm"
    - "a scheme for molecular computation of maximum likelihood estimators for log linear models"
    - "approximate analysis of biological systems by hybrid switching jump diffusion"

Connected through hyperedge HE-4 (induced by source paper "experimental biological protocols with formal semantics"):
  with members:
    - "synthesizing and tuning chemical reaction networks with specified behaviours"
    - "statistical model checking an overview"

Connected through hyperedge HE-5 (induced by source paper "time space trade offs in molecular computation"):
  with members:
    - "plurality consensus via shuffling lessons learned from load balancing"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "stable leader election in population protocols requires linear time"
    - "determining majority in networks with local interactions and very small local memory"
    - "timing in chemical reaction networks"
    - "passively mobile communicating machines that use restricted space"

Connected through hyperedge HE-6 (induced by source paper "time space trade offs in population protocols"):
  with members:
    - "stable leader election in population protocols requires linear time"
    - "timing in chemical reaction networks"
    - "passively mobile communicating machines that use restricted space"

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.DC(Distributed, Parallel, and Cluster Computing)