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: "adaptive uncertainty resolution in bayesian combinatorial optimization problems"
  Abstract: In several applications such as databases, planning, and sensor networks, parameters such as selectivity, load, or sensed values are known only with some associated uncertainty. The performance of such a system (as captured by some objective function over the parameters) is significantly improved if some of these parameters can be probed or observed. In a resource constrained situation, deciding which parameters to observe in order to optimize system performance itself becomes an interesting and important optimization problem. This general problem is the focus of this paper. #R##N#One of the most important considerations in this framework is whether adaptivity is required for the observations. Adaptive observations introduce blocking or sequential operations in the system whereas non-adaptive observations can be performed in parallel. One of the important questions in this regard is to characterize the benefit of adaptivity for probes and observation. #R##N#We present general techniques for designing constant factor approximations to the optimal observation schemes for several widely used scheduling and metric objective functions. We show a unifying technique that relates this optimization problem to the outlier version of the corresponding deterministic optimization. By making this connection, our technique shows constant factor upper bounds for the benefit of adaptivity of the observation schemes. We show that while probing yields significant improvement in the objective function, being adaptive about the probing is not beneficial beyond constant factors.

Connected through hyperedge HE-1 (induced by source paper "the markovian price of information"):
  with members:
    - "the price of information in combinatorial optimization"
    - "optimal data acquisition for statistical estimation"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "descending price optimally coordinates search"
    - "descending price coordinates approximately efficient search"
    - "online contention resolution schemes"
    - "improved approximation algorithms for stochastic matching"
    - "submodular stochastic probing on matroids"

Connected through hyperedge HE-2 (induced by source paper "on the complexity of restoring corrupted colorings"):
  with members:
    - "on the hardness of losing weight"
    - "fine grained complexity analysis of two classic tsp variants"
    - "fixing improper colorings of graphs"
    - "finding shortest paths between graph colourings"
    - "reconfiguration in bounded bandwidth and treedepth"
    - "kernelization lower bounds by cross composition"

Connected through hyperedge HE-3 (induced by source paper "distance sensitive planar point location"):
  with members:
    - "entropy triangulation and point location in planar subdivisions"
    - "a static optimality transformation with applications to planar point location"

Connected through hyperedge HE-4 (induced by source paper "stability of adversarial routing with feedback"):
  with members:
    - "broadcasting in ad hoc multiple access channels"
    - "stability of the max weight protocol in adversarial wireless networks"
    - "maximum throughput of multiple access channels in adversarial environments"

Connected through hyperedge HE-5 (induced by source paper "on resilient graph spanners"):
  with members:
    - "robust geometric spanners"
    - "fault tolerant spanners better and simpler"

Connected through hyperedge HE-6 (induced by source paper "tree nash equilibria in the network creation game"):
  with members:
    - "bounded distance network creation games"

Connected through hyperedge HE-7 (induced by source paper "maximizing expected utility for stochastic combinatorial optimization problems"):
  with members:
    - "stochastic combinatorial optimization via poisson approximation"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "when lp is the cure for your matching woes approximating stochastic matchings"
    - "a unified approach to ranking in probabilistic databases"
    - "consensus answers for queries over probabilistic databases"
    - "stochastic combinatorial optimization under probabilistic constraints"

Connected through hyperedge HE-8 (induced by source paper "a new approximation technique for resource allocation problems"):
  with members:
    - "matroidal degree bounded minimum spanning trees"
    - "on generalizations of network design problems with degree bounds"
    - "santa claus schedules jobs on unrelated machines"
    - "approximating scheduling machines with capacity constraints"
    - "on allocating goods to maximize fairness"
    - "scheduling with outliers"

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)