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 "hardness of computing and approximating predicates and functions with leaderless population protocols"):
  Abstract: Population protocols are a distributed computing model appropriate for describing massive numbers of agents with limited computational power. A population protocol "has an initial leader" if every valid initial configuration contains a single agent in a special "leader" state that helps to coordinate the computation. Although the class of predicates and functions computable with probability 1 is the same whether or not there is an initial leader (semilinear functions and predicates), it is not known whether a leader is necessary for fast computation. Efficient population protocols are generally defined as those computing in polylogarithmic in $n$ (parallel) time. We consider leaderless population protocols, regarding the computation finished when a configuration is reached from which a different output is no longer reachable. #R##N#In this setting we show that a wide class of functions and predicates computable by population protocols are not efficiently computable (they require at least linear time to stabilize on a correct answer), nor are some linear functions even efficiently approximable. For example, the widely studied parity, majority, and equality predicates cannot be computed in sublinear time. Moreover, it requires at least linear time for a population protocol even to approximate any linear function with a coefficient outside of $\mathbb{N}$: for sufficiently small $\gamma > 0$, the output of a sublinear time protocol can stabilize outside the interval $f(m) (1 \pm \gamma)$ on infinitely many inputs $m$. We also show that it requires linear time to exactly compute a wide range of semilinear functions (e.g., $f(m)=m$ if $m$ is even and $2m$ if $m$ is odd). #R##N#Finally, we show that with a sufficiently large value of $\gamma$, a population protocol can approximate any linear $f$ with nonnegative rational coefficients, within approximation factor $\gamma$, in $O(\log n)$ time.

Includes members:
  Member "fast space optimal leader election in population protocols":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust comparison in population protocols")
  Member "robustness of expressivity in chemical reaction networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on analog models of computation")
  Member "democratic existential and consensus based output conventions in stable computation by chemical reaction networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "computing with chemical reaction networks a tutorial")
  Member "how many cooks spoil the soup":
    also connected through other hyperedges:
      (none)
  Member "stable leader election in population protocols requires linear time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "constructive expressive power of population protocols")
  Member "leaderless deterministic chemical reaction networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "composable computation in discrete chemical reaction networks")
  Member "timing in chemical reaction networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "communication cost of consensus for nodes with limited memory")
  Member "deterministic function computation with chemical reaction networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed voting in beep model")

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.DC(Distributed, Parallel, and Cluster Computing)