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: "multidimensional binary search for contextual decision making"
  Abstract: We consider a multidimensional search problem that is motivated by questions in contextual decision-making, such as dynamic pricing and personalized medicine. Nature selects a state from a $d$-dimensional unit ball and then generates a sequence of $d$-dimensional directions. We are given access to the directions, but not access to the state. After receiving a direction, we have to guess the value of the dot product between the state and the direction. Our goal is to minimize the number of times when our guess is more than $\epsilon$ away from the true answer. We construct a polynomial time algorithm that we call Projected Volume achieving regret $O(d\log(d/\epsilon))$, which is optimal up to a $\log d$ factor. The algorithm combines a volume cutting strategy with a new geometric technique that we call cylindrification.

Connected through hyperedge HE-1 (induced by source paper "corrupted multidimensional binary search learning in the presence of irrational agents"):
  with members:
    - "stochastic linear optimization with adversarial corruption"
    - "corruption robust exploration in episodic reinforcement learning"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "the intrinsic robustness of stochastic bandits to strategic manipulation"
    - "semi parametric dynamic contextual pricing"
    - "manipulating a learning defender and ways to counteract"
    - "contextual search via intrinsic volumes"
    - "stochastic bandits robust to adversarial corruptions"

Connected through hyperedge HE-2 (induced by source paper "dynamic incentive aware learning robust pricing in contextual auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "contextual search via intrinsic volumes"
    - "multi armed bandit problems with strategic arms"
    - "perishability of data dynamic pricing under varying coefficient models"
    - "a field guide to personalized reserve prices"
    - "a nearly linear time ptas for explicit fractional packing and covering linear programs"
    - "learning prices for repeated auctions with strategic buyers"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"

Connected through hyperedge HE-3 (induced by source paper "online pricing with reserve price constraint for personal data markets"):
  with members:
    - "introduction to online convex optimization"
    - "contextual search via intrinsic volumes"
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "the design of arbitrage free data pricing schemes"
    - "oracle efficient online learning and auction design"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "watch and learn optimizing from revealed preferences feedback"

Connected through hyperedge HE-4 (induced by source paper "online learning with an unknown fairness metric"):
  with members:
    - "probably approximately metric fair learning"
    - "fairness through computationally bounded awareness"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "calibrated fairness in bandits"
    - "calibration for the computationally identifiable masses"
    - "fairness in reinforcement learning"
    - "fairness in learning classic and contextual bandits"
    - "on the im possibility of fairness"

Connected through hyperedge HE-5 (induced by source paper "contextual search via intrinsic volumes"):
  with members:
    - "online learning with an unknown fairness metric"
    - "perishability of data dynamic pricing under varying coefficient models"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "living on the edge phase transitions in convex programs with random data"

Connected through hyperedge HE-6 (induced by source paper "perishability of data dynamic pricing under varying coefficient models"):
  with members:
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing with non gaussian measurements"
    - "one bit compressed sensing by linear programming"

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)