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: "local distributed algorithms in highly dynamic networks"
  Abstract: The present paper studies local distributed graph problems in highly dynamic networks. We define a (in our view) natural generalization of static graph problems to the dynamic graph setting. For some parameter $T>0$, the set of admissible outputs of nodes in a $T$-dynamic solution for a given graph problem at some time $t$ is defined by the dynamic graph topology in the time interval $[t-T,t]$. The guarantees of a $T$-dynamic solution become stronger the more stable the graph remains during the interval $[t-T,t]$ and they coincide with the definition of the static graph problem if the graph is static throughout the interval. We further present an abstract framework that allows to develop distributed algorithms for a given dynamic graph problem. For some $T>0$, the algorithms always output a valid $T$-dynamic solution of the given graph problem. Further, if a constant neighborhood around some part of the graph is stable during an interval $[t_1,t_2]$, the algorithms compute a static solution for this part of the graph throughout the interval $[t_1+T',t_2]$ for some $T'>0$. Ideally $T$ and $T'$ are of the same asymptotic order as the time complexity for solving the given graph problem in static networks. #R##N#We apply our generic framework to two classic distributed symmetry breaking problems: the problem of computing a (degree+1)-vertex coloring and the problem of computing a maximal independent set (MIS) of the network graph. For both problems, we obtain distributed algorithms that always output a valid $O(\log n)$-dynamic solution. Further, if some part of the graph and its $O(1)$-neighborhood remain stable for some interval $[t_1,t_2]$, for the given part of the graph, the algorithms compute a valid static solution for the two problems that remains stable throughout an interval $[t_1+O(\log n),t_2]$.

Connected through hyperedge HE-1 (induced by source paper "fast and simple deterministic algorithms for highly dynamic networks"):
  with members:
    - "lower bounds for maximal matchings and maximal independent sets"
    - "how much does randomness help with locally checkable problems"
    - "simple dynamic algorithms for maximal independent set and other problems"
    - "fully dynamic maximal independent set with sublinear update time"
    - "improved algorithms for fully dynamic maximal independent set"
    - "improved deterministic distributed matching via rounding"
    - "new classes of distributed time complexity"
    - "dynamic algorithms for graph coloring"

Connected through hyperedge HE-2 (induced by source paper "distributed detection of cliques in dynamic networks"):
  with members:
    - "fast and simple deterministic algorithms for highly dynamic networks"
    - "simple dynamic algorithms for maximal independent set and other problems"
    - "fully dynamic maximal independent set with sublinear update time"
    - "distributed triangle detection via expander decomposition"
    - "improved algorithms for fully dynamic maximal independent set"
    - "detecting cliques in congest networks"
    - "distributed subgraph detection"
    - "fooling views a new lower bound technique for distributed computations under congestion"

Connected through hyperedge HE-3 (induced by source paper "efficient deterministic distributed coloring with small bandwidth"):
  with members:
    - "faster deterministic distributed coloring through recursive list coloring"
    - "on the use of randomness in local distributed graph algorithms"
    - "deterministic distributed dominating set approximation in the congest model"
    - "delta 1 coloring in the congested clique model"
    - "the complexity of delta 1 coloring incongested clique massively parallel computation and centralized local computation"
    - "quadratic and near quadratic lower bounds for the congest model"
    - "adapting sequential algorithms to the distributed setting"
    - "on derandomizing local distributed algorithms"

Connected through hyperedge HE-4 (induced by source paper "robustness a new form of heredity motivated by dynamic networks"):
  with members:
    - "temporal vertex cover with a sliding time window"
    - "finding structure in dynamic networks"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "enabling minimal dominating set in highly dynamic distributed systems"
    - "the next 700 impossibility results in time varying graphs"
    - "on the feasibility of maintenance algorithms in dynamic graphs"
    - "time varying graphs and dynamic 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.DS(Data Structures and Algorithms)