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: "election vs selection two ways of finding the largest node in a graph"
  Abstract: Finding the node with the largest label in a network, modeled as an undirected connected graph, is one of the fundamental problems in distributed computing. This is the way in which $\textit{leader election}$ is usually solved. We consider two distinct tasks in which the largest-labeled node is found deterministically. In $\textit{selection}$, this node must output 1 and all other nodes must output 0. In $\textit{election}$, the other nodes must additionally learn the largest label. Our aim is to compare the difficulty of these two tasks executed under stringent running time constraints. The measure of difficulty is the amount of information that nodes of the network must initially possess in order to solve the given task in an imposed amount of time. Following the standard framework of $\textit{algorithms with advice}$, this information (a single binary string) is provided to all nodes at the start by an oracle knowing the entire graph. The length of this string is called the $\textit{size of advice}$. Consider the class of $n$-node graphs with any diameter $diam \leq D$. If time is larger than $diam$, then both tasks can be solved without advice. For the task of $\textit{election}$, we show that if time is smaller than $diam$, then the optimal size of advice is $\Theta(\log n)$, and if time is exactly $diam$, then the optimal size of advice is $\Theta(\log D)$. For the task of $\textit{selection}$, the situation changes dramatically, even within the class of rings. Indeed, for the class of rings, we show that, if time is $O(diam^{\epsilon})$, for any $\epsilon <1$, then the optimal size of advice is $\Theta(\log D)$, and, if time is $\Theta(diam)$ (and at most $diam$) then this optimal size is $\Theta(\log \log D)$.

Connected through hyperedge HE-1 (induced by source paper "impact of knowledge on election time in anonymous networks"):
  with members:
    - "deterministic leader election in o d log n time with messages of size o 1"
    - "topology recognition with advice"
    - "time vs information tradeoffs for leader election in anonymous trees"
    - "knowledge level of symmetry and time of leader election"
    - "leader election for anonymous asynchronous agents in arbitrary networks"

Connected through hyperedge HE-2 (induced by source paper "topology recognition and leader election in colored networks"):
  with members:
    - "time vs information tradeoffs for leader election in anonymous trees"
    - "knowledge level of symmetry and time of leader election"
    - "leader election for anonymous asynchronous agents in arbitrary networks"

Connected through hyperedge HE-3 (induced by source paper "time vs information tradeoffs for leader election in anonymous trees"):
  with members:
    - "knowledge level of symmetry and time of leader election"
    - "leader election for anonymous asynchronous agents in arbitrary 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)