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: "approximation algorithms for optimal decision trees and adaptive tsp problems"
  Abstract: We consider the problem of constructing optimal decision trees: given a collection of tests that can disambiguate between a set of m possible diseases, each test having a cost, and the a priori likelihood of any particular disease, what is a good adaptive strategy to perform these tests to minimize the expected cost to identify the disease? This problem has been studied in several works, with O(log m)-approximations known in the special cases when either costs or probabilities are uniform. In this paper, we settle the approximability of the general problem by giving a tight O(log m)-approximation algorithm. We also consider a substantial generalization, the adaptive traveling salesman problem. Given an underlying metric space, a random subset S of vertices is drawn from a known distribution, but S is initially unknown—we get information about whether any vertex is in S only when it is visited. What is a good adaptive strategy to visit all vertices in the random subset S while minimizing the expected dista...

Connected through hyperedge HE-1 (induced by source paper "a tight analysis of greedy yields subexponential time approximation for uniform decision tree"):
  with members:
    - "generalized binary search for split neighborly problems"
    - "inapproximability of vc dimension and littlestone s dimension"
    - "inapproximability of nash equilibrium"
    - "efficient test selection in active diagnosis via entropy approximation"
    - "noisy bayesian active learning"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "average case active learning with costs"

Connected through hyperedge HE-2 (induced by source paper "adaptive algorithm for finding connected dominating sets in uncertain graphs"):
  with members:
    - "approximation algorithm for minimum weight fault tolerant virtual backbone in unit disk graphs"
    - "the power of local information in social networks"

Connected through hyperedge HE-3 (induced by source paper "generalized binary search for split neighborly problems"):
  with members:
    - "efficient test selection in active diagnosis via entropy approximation"
    - "comparison based learning with rank nets"

Connected through hyperedge HE-4 (induced by source paper "data driven planning via imitation learning"):
  with members:
    - "deep reinforcement learning a brief survey"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "learning sampling distributions for robot motion planning"
    - "near optimal edge evaluation in explicit generalized binomial graphs"
    - "qmdp net deep learning for planning under partial observability"
    - "learning from the hindsight plan episodic mpc improvement"
    - "deep reinforcement learning for dialogue generation"
    - "pomdp lite for robust robot planning under uncertainty"

Connected through hyperedge HE-5 (induced by source paper "adaptive information gathering via imitation learning"):
  with members:
    - "pomdp lite for robust robot planning under uncertainty"
    - "near optimal bayesian active learning with correlated and noisy tests"
    - "plato policy learning using adaptive trajectory optimization"
    - "learning to gather information via imitation"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "active classification theory and application to underwater inspection"
    - "near optimal bayesian active learning with noisy observations"

Connected through hyperedge HE-6 (induced by source paper "adaptive submodular ranking"):
  with members:
    - "scenario submodular cover"
    - "near optimal bayesian active learning for decision making"
    - "submodular meets structured finding diverse subsets in exponentially large structured item sets"
    - "analytical approach to parallel repetition"
    - "a note on the generalized min sum set cover problem"
    - "near optimal bayesian active learning with noisy observations"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "learning to gather information via imitation"):
  with members:
    - "near optimal bayesian active learning with correlated and noisy tests"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "efficient touch based localization through submodularity"
    - "active classification theory and application to underwater inspection"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "scenario submodular cover"):
  with members:
    - "adaptive submodular ranking"
    - "near optimal bayesian active learning for decision making"
    - "approximation algorithms for stochastic boolean function evaluation and stochastic submodular set cover"
    - "a note on the generalized min sum set cover problem"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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)