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: "space optimal majority in population protocols"
  Abstract: Population protocols are a model of distributed computing, in which $n$ agents with limited local state interact randomly, and cooperate to collectively compute global predicates. An extensive series of papers, across different communities, has examined the computability and complexity characteristics of this model. Majority, or consensus, is a central task, in which agents need to collectively reach a decision as to which one of two states $A$ or $B$ had a higher initial count. Two complexity metrics are important: the time that a protocol requires to stabilize to an output decision, and the state space size that each agent requires. #R##N#It is known that majority requires $\Omega(\log \log n)$ states per agent to allow for poly-logarithmic time stabilization, and that $O(\log^2 n)$ states are sufficient. Thus, there is an exponential gap between the upper and lower bounds. #R##N#We address this question. We provide a new lower bound of $\Omega(\log n)$ states for any protocol which stabilizes in $O( n^{1-c} )$ time, for any $c > 0$ constant. This result is conditional on basic monotonicity and output assumptions, satisfied by all known protocols. Technically, it represents a significant departure from previous lower bounds. Instead of relying on dense configurations, we introduce a new surgery technique to construct executions which contradict the correctness of algorithms that stabilize too fast. Subsequently, our lower bound applies to general initial configurations. #R##N#We give an algorithm for majority which uses $O(\log n)$ states, and stabilizes in $O(\log^2 n)$ time. Central to the algorithm is a new leaderless phase clock, which allows nodes to synchronize in phases of $\Theta(n \log{n})$ consecutive interactions using $O(\log n)$ states per node. We also employ our phase clock to build a leader election algorithm with $O(\log n )$ states, which stabilizes in $O(\log^2 n)$ time.

Connected through hyperedge HE-1 (induced by source paper "robust comparison in population protocols"):
  with members:
    - "majority stabilization in population protocols"
    - "fast space optimal leader election in population protocols"
    - "robust detection in leak prone population protocols"
    - "time space trade offs in population protocols"
    - "determining majority in networks with local interactions and very small local memory"
    - "passively mobile communicating machines that use restricted space"
    - "the computational power of population protocols"

Connected through hyperedge HE-2 (induced by source paper "popsgd decentralized stochastic gradient descent in the population model"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "decentralization meets quantization"
    - "stochastic gradient push for distributed deep learning"
    - "scaling sgd batch size to 32k for imagenet training"
    - "sparse communication for distributed gradient descent"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-3 (induced by source paper "noidy conmunixatipn on the convergence of the averaging population protocol"):
  with members:
    - "almost logarithmic time space optimal leader election in population protocols"
    - "fast space optimal leader election in population protocols"
    - "erasure correction for noisy radio networks"
    - "limits for rumor spreading in stochastic populations"
    - "ignore or comply on breaking symmetry in consensus"
    - "broadcasting in noisy radio networks"
    - "rapid asynchronous plurality consensus"
    - "efficient k party voting with two choices"

Connected through hyperedge HE-4 (induced by source paper "composable computation in discrete chemical reaction networks"):
  with members:
    - "efficient size estimation and impossibility of termination in uniform dense population protocols"
    - "output oblivious stochastic chemical reaction networks"
    - "fast space optimal leader election in population protocols"
    - "composable rate independent computation in continuous chemical reaction networks"
    - "hardness of computing and approximating predicates and functions with leaderless population protocols"
    - "time space trade offs in molecular computation"
    - "time space trade offs in population protocols"
    - "stable leader election in population protocols requires linear time"

Connected through hyperedge HE-5 (induced by source paper "communication cost of consensus for nodes with limited memory"):
  with members:
    - "global majority consensus by local majority polling on graphs of a given degree sequence"
    - "population protocols are fast"
    - "population protocols for leader election and exact majority with o log 2 n states and o log 2 n convergence time"
    - "time space trade offs in population protocols"
    - "discordant voting processes on finite graphs"
    - "stable leader election in population protocols requires linear time"
    - "plurality consensus in the gossip model"
    - "determining majority in networks with local interactions and very small local memory"

Connected through hyperedge HE-6 (induced by source paper "on counting the population size"):
  with members:
    - "exact size counting in uniform population protocols in nearly logarithmic time"
    - "efficient size estimation and impossibility of termination in uniform dense population protocols"
    - "fast space optimal leader election in population protocols"
    - "simple load balancing"
    - "time space trade offs in population protocols"
    - "stable leader election in population protocols requires linear time"
    - "determining majority in networks with local interactions and very small local memory"
    - "the computational power of population protocols"

Connected through hyperedge HE-7 (induced by source paper "expressive power of broadcast consensus protocols"):
  with members:
    - "affine extensions of integer vector addition systems with states"
    - "controlling a population"
    - "time space trade offs in population protocols"
    - "context free commutative grammars with integer counters and resets"
    - "the power of well structured systems"
    - "the computational power of population protocols"

Connected through hyperedge HE-8 (induced by source paper "broadcast distributed voting algorithm in population protocols"):
  with members:
    - "distributed voting ranking with optimal number of states per node"
    - "time space trade offs in population protocols"
    - "time and space optimal counting in population protocols"
    - "stable leader election in population protocols requires linear time"
    - "deterministic function computation with chemical reaction networks"

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)