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: "time and space optimal counting in population protocols"
  Abstract: This work concerns the general issue of combined optimality in terms of time and space complexity. In this context, we study the problem of (exact) counting resource-limited and passively mobile nodes in the model of population protocols, in which the space complexity is crucial. The counted nodes are memory-limited anonymous devices (called agents) communicating asynchronously in pairs (according to a fairness condition). Moreover, we assume that these agents are prone to failures so that they cannot be correctly initialized. This study considers two classical fairness conditions, and for each we investigate the issue of time optimality of counting given the optimal space per agent. In the case of randomly interacting agents (probabilistic fairness), as usual, the convergence time is measured in terms of parallel time (or parallel interactions), which is defined as the number of pairwise interactions until convergence, divided by n (the number of agents). In case of weak fairness, where it is only required that every pair of agents interacts infinitely often, the convergence time is defined in terms of non-null transitions, i.e, the transitions that affect the states of the interacting agents.First, assuming probabilistic fairness, we present a "non-guessing" time optimal protocol of O(n log n) expected time given an optimal space of only one bit, and we prove the time optimality of this protocol. Then, for weak fairness, we show that a space optimal (semi-uniform) solution cannot converge faster than in $\Omega$(2^n) time (non-null transitions). This result, together with the time complexity analysis of an already known space optimal protocol, shows that it is also optimal in time (given the optimal space constrains).

Connected through hyperedge HE-1 (induced by source paper "on the necessary memory to compute the plurality in multi agent systems"):
  with members:
    - "distributed voting ranking with optimal number of states per node"
    - "tight analysis for the 3 majority consensus dynamics"
    - "stabilizing consensus with many opinions"
    - "plurality consensus in the gossip model"
    - "determining majority in networks with local interactions and very small local memory"
    - "timing in chemical reaction networks"
    - "the computational power of population protocols"

Connected through hyperedge HE-2 (induced by source paper "broadcast distributed voting algorithm in population protocols"):
  with members:
    - "distributed voting ranking with optimal number of states per node"
    - "space optimal majority in population protocols"
    - "time space trade offs in population protocols"
    - "stable leader election in population protocols requires linear time"
    - "deterministic function computation with chemical reaction networks"

Connected through hyperedge HE-3 (induced by source paper "efficient size estimation and impossibility of termination in uniform dense population protocols"):
  with members:
    - "on counting the population size"
    - "population protocols are fast"
    - "almost logarithmic time space optimal leader election in population protocols"
    - "fast space optimal leader election in population protocols"
    - "logarithmic expected time leader election in population protocol model"
    - "hardness of computing and approximating predicates and functions with leaderless population protocols"
    - "space optimal majority in population protocols"
    - "time space trade offs in population protocols"

Connected through hyperedge HE-4 (induced by source paper "population stability regulating size in the presence of an adversary"):
  with members:
    - "space optimal majority in population protocols"
    - "time space trade offs in population protocols"
    - "distributed house hunting in ant colonies"
    - "the computational power of population protocols"

Connected through hyperedge HE-5 (induced by source paper "stable leader election in population protocols requires linear time"):
  with members:
    - "time space trade offs in molecular computation"
    - "time space trade offs in population protocols"
    - "timing in chemical reaction networks"
    - "deterministic function computation with chemical reaction networks"
    - "the computational power of population protocols"

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)