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: "competitive broadcast against adaptive adversary in multi channel radio networks"
  Abstract: Wireless networks are vulnerable to adversarial jamming due to the open nature of the communication medium. To thwart such malicious behavior, researchers have proposed resource competitive analysis. In this framework, sending, listening, or jamming on one channel for one time slot costs one unit of energy. The adversary can employ arbitrary jamming strategy to disrupt communication, but has a limited energy budget $T$. The honest nodes, on the other hand, aim to accomplish the distributed computing task in concern with a spending of $o(T)$. #R##N#In this paper, we focus on solving the broadcast problem, in which a single source node wants to disseminate a message to all other $n-1$ nodes. Previous work have shown, in single-hop single-channel scenario, each node can receive the message in $\tilde{O}(T+n)$ time, while spending only $\tilde{O}(\sqrt{T/n}+1)$ energy. If $C$ channels are available, then the time complexity can be reduced by a factor of $C$, without increasing nodes' cost. However, these multi-channel algorithms only work for certain values of $n$ and $C$, and can only tolerate an oblivious adversary. #R##N#We develop two new resource competitive algorithms for the broadcast problem. They work for arbitrary $n,C$ values, require minimal prior knowledge, and can tolerate a powerful adaptive adversary. In both algorithms, each node's runtime is dominated by the term $O(T/C)$, and each node's energy cost is dominated by the term $\tilde{O}(\sqrt{T/n})$. The time complexity is asymptotically optimal, while the energy complexity is near optimal in some cases. We use "epidemic broadcast" to achieve time efficiency and resource competitiveness, and employ the coupling technique in the analysis to handle the adaptivity of the adversary. These tools might be of independent interest, and can potentially be applied in the design and analysis of other resource competitive algorithms.

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)