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: "contextual search via intrinsic volumes"
  Abstract: We study the problem of contextual search, a multidimensional generalization of binary search that captures many problems in contextual decision-making. In contextual search, a learner is trying to learn the value of a hidden vector $v \in [0,1]^d$. Every round the learner is provided an adversarially-chosen context $u_t \in \mathbb{R}^d$, submits a guess $p_t$ for the value of $\langle u_t, v\rangle$, learns whether $p_t < \langle u_t, v\rangle$, and incurs loss $\ell(\langle u_t, v\rangle, p_t)$ (for some loss function $\ell$). The learner's goal is to minimize their total loss over the course of $T$ rounds. #R##N#We present an algorithm for the contextual search problem for the symmetric loss function $\ell(\theta, p) = |\theta - p|$ that achieves $O_{d}(1)$ total loss. We present a new algorithm for the dynamic pricing problem (which can be realized as a special case of the contextual search problem) that achieves $O_{d}(\log \log T)$ total loss, improving on the previous best known upper bounds of $O_{d}(\log T)$ and matching the known lower bounds (up to a polynomial dependence on $d$). Both algorithms make significant use of ideas from the field of integral geometry, most notably the notion of intrinsic volumes of a convex set. To the best of our knowledge this is the first application of intrinsic volumes to algorithm design.

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"
    - "stochastic bandits robust to adversarial corruptions"
    - "tsallis inf an optimal algorithm for stochastic and adversarial bandits"

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"
    - "multi armed bandit problems with strategic arms"
    - "perishability of data dynamic pricing under varying coefficient models"
    - "a field guide to personalized reserve prices"
    - "multidimensional binary search for contextual decision making"
    - "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 "contextual search for general hypothesis classes"):
  with members:
    - "corrupted multidimensional binary search learning in the presence of irrational agents"
    - "online learning with an unknown fairness metric"
    - "bandits and experts in metric spaces"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "contextual bandits with similarity information"

Connected through hyperedge HE-4 (induced by source paper "reserve pricing in repeated second price auctions with strategic bidders"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "optimal pricing in repeated posted price auctions"
    - "learning optimal reserve price against non myopic bidders"
    - "robust repeated auctions under heterogeneous buyer behavior"
    - "optimal dynamic auctions are virtual welfare maximizers"
    - "dynamic pricing with finitely many unknown valuations"
    - "on consistency of optimal pricing algorithms in repeated posted price auctions with strategic buyer"
    - "revenue optimization with approximate bid predictions"

Connected through hyperedge HE-5 (induced by source paper "online pricing with reserve price constraint for personal data markets"):
  with members:
    - "introduction to online convex optimization"
    - "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"
    - "multidimensional binary search for contextual decision making"
    - "watch and learn optimizing from revealed preferences feedback"

Connected through hyperedge HE-6 (induced by source paper "incentive aware contextual pricing with non parametric market noise"):
  with members:
    - "dynamic incentive aware learning robust pricing in contextual auctions"
    - "the intrinsic robustness of stochastic bandits to strategic manipulation"
    - "lp based approximation for personalized reserve prices"
    - "semi parametric dynamic contextual pricing"
    - "nonparametric learning and optimization with covariates"
    - "learning optimal reserve price against non myopic bidders"
    - "ironing in the dark"
    - "a nearly linear time ptas for explicit fractional packing and covering linear programs"

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)