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: "sublinear bounds for randomized leader election"
  Abstract: This paper concerns {\em randomized} leader election in synchronous distributed networks. A distributed leader election algorithm is presented for complete $n$-node networks that runs in O(1) rounds and (with high probability) uses only $O(\sqrt{n}\log^{3/2} n)$ messages to elect a unique leader (with high probability). When considering the "explicit" variant of leader election where eventually every node knows the identity of the leader, our algorithm yields the asymptotically optimal bounds of O(1) rounds and O(n) messages. This algorithm is then extended to one solving leader election on any connected non-bipartite $n$-node graph $G$ in $O(\tau(G))$ time and $O(\tau(G)\sqrt{n}\log^{3/2} n)$ messages, where $\tau(G)$ is the mixing time of a random walk on $G$. The above result implies highly efficient (sublinear running time and messages) leader election algorithms for networks with small mixing times, such as expanders and hypercubes. In contrast, previous leader election algorithms had at least linear message complexity even in complete graphs. Moreover, super-linear message lower bounds are known for time-efficient {\em deterministic} leader election algorithms. #R##N#Finally, we present an almost matching lower bound for randomized leader election, showing that $\Omega(\sqrt n)$ messages are needed for any leader election algorithm that succeeds with probability at least $1/e + \eps$, for any small constant $\eps > 0$. #R##N#We view our results as a step towards understanding the randomized complexity ofleader election in distributed networks.

Connected through hyperedge HE-1 (induced by source paper "message reduction in the local model is a free lunch"):
  with members:
    - "time message trade offs in distributed algorithms"
    - "broadcast and minimum spanning tree with o m messages in the asynchronous congest model"
    - "distributed spanner approximation"
    - "efficient algorithms for constructing very sparse spanners and emulators"
    - "on the complexity of local distributed graph problems"
    - "a fast network decomposition algorithm and its applications to constant time distributed computation"
    - "construction and impromptu repair of an mst in a distributed network with o m communication"
    - "improved parallel algorithms for spanners and hopsets"

Connected through hyperedge HE-2 (induced by source paper "scalable and secure computation among strangers resource competitive byzantine protocols"):
  with members:
    - "time message trade offs in distributed algorithms"
    - "sending a message with unknown noise"
    - "torbricks blocking resistant tor bridge distribution"
    - "interactive communication with unknown noise rate"
    - "bitcoin ng a scalable blockchain protocol"
    - "making evildoers pay resource competitive broadcast in sensor networks"
    - "breaking the o n 2 bit barrier scalable byzantine agreement with an adaptive adversary"

Connected through hyperedge HE-3 (induced by source paper "leader election in well connected graphs"):
  with members:
    - "the complexity of leader election a chasm at diameter two"
    - "a time and message optimal distributed algorithm for minimum spanning trees"
    - "distributed computation of mixing time"
    - "distributed elections in an archimedean ring of processors"

Connected through hyperedge HE-4 (induced by source paper "the complexity of leader election a chasm at diameter two"):
  with members:
    - "symmetry breaking in the congest model time and message efficient algorithms for ruling sets"

Connected through hyperedge HE-5 (induced by source paper "deterministic leader election takes theta d log n bit rounds"):
  with members:
    - "impact of knowledge on election time in anonymous networks"
    - "knowledge level of symmetry and time of leader election"

Connected through hyperedge HE-6 (induced by source paper "fast distributed algorithms for connectivity and mst in large graphs"):
  with members:
    - "near optimal clustering in the k machine model"
    - "tight bounds for distributed graph computations"
    - "on the distributed complexity of large scale graph computations"
    - "a time and message optimal distributed algorithm for minimum spanning trees"
    - "super fast mst algorithms in the congested clique using o m messages"
    - "distributed algorithms for finding local clusters using heat kernel pagerank"
    - "algebraic methods in the congested clique"
    - "construction and impromptu repair of an mst in a distributed network with o m communication"

Connected through hyperedge HE-7 (induced by source paper "distributed symmetry breaking in hypergraphs"):
  with members:
    - "near optimal distributed approximation of minimum weight connected dominating set"
    - "on computing maximal independent sets of hypergraphs in parallel"
    - "distributed verification and hardness of distributed approximation"
    - "local computation lower and upper bounds"

Connected through hyperedge HE-8 (induced by source paper "distributed computation of large scale graph problems"):
  with members:
    - "fast distributed pagerank computation"
    - "distributed approximation algorithms for weighted shortest paths"
    - "when distributed computation is communication expensive"
    - "can quantum communication speed up distributed computation"
    - "streaming balanced graph partitioning for random graphs"
    - "tri tri again finding triangles and small subgraphs in a distributed setting"
    - "dense subgraphs on dynamic networks"
    - "distributed verification and hardness of distributed approximation"

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.DS(Data Structures and Algorithms)