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: "chernoff hoeffding inequality and applications"
  Abstract: When dealing with modern big data sets, a very common theme is reducing the set through a random process. These generally work by making "many simple estimates" of the full data set, and then judging them as a whole. Perhaps magically, these "many simple estimates" can provide a very accurate and small representation of the large data set. The key tool in showing how many of these simple estimates are needed for a fixed accuracy trade-off is the Chernoff-Hoeffding inequality[Che52,Hoe63]. This document provides a simple form of this bound, and two examples of its use.

Connected through hyperedge HE-1 (induced by source paper "runtime performances of randomized search heuristics for the dynamic weighted vertex cover problem"):
  with members:
    - "parameterized analysis of multi objective evolutionary algorithms and the weighted vertex cover problem"
    - "on the runtime of randomized local search and simple evolutionary algorithms for dynamic makespan scheduling"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "multiplicative drift analysis"

Connected through hyperedge HE-2 (induced by source paper "throughput region of spatially correlated interference packet networks"):
  with members:
    - "linear degrees of freedom of mimo broadcast channels with reconfigurable antennas in the absence of csit"
    - "optimal sum dof of the k user miso bc with current and delayed feedback"
    - "optimal dof of the k user broadcast channel with delayed and imperfect current csit"
    - "when does spatial correlation add value to delayed channel state information"
    - "two user erasure interference channels with local delayed csit"
    - "transmit beamforming for miso broadcast channels with statistical and delayed csit"
    - "informational bottlenecks in two unicast wireless networks with delayed csit"
    - "secure degrees of freedom region of the two user miso broadcast channel with alternating csit"

Connected through hyperedge HE-3 (induced by source paper "capacity results for binary fading interference channels with delayed csit"):
  with members:
    - "two hop interference channels impact of linear schemes"
    - "binary fading interference channel with no csit"
    - "communication through collisions opportunistic utilization of past receptions"
    - "approximate capacity region of the two user miso broadcast channels with delayed csit"
    - "two hop interference channels impact of linear time varying schemes"
    - "on the degrees of freedom of k user siso interference and x channels with delayed csit"
    - "degrees of freedom region of the mimo interference channel with output feedback and delayed csit"
    - "degrees of freedom of two hop wireless networks everyone gets the entire cake"

Connected through hyperedge HE-4 (induced by source paper "communication through collisions opportunistic utilization of past receptions"):
  with members:
    - "capacity results for binary fading interference channels with delayed csit"
    - "on the degrees of freedom of k user siso interference and x channels with delayed csit"
    - "on achieving local view capacity via maximal independent graph scheduling"
    - "completely stale transmitter channel state information is still very useful"
    - "retrospective interference alignment"
    - "on the capacity of multi hop wireless networks with partial network knowledge"

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)