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: "deterministic and probabilistic binary search in graphs"
  Abstract: We consider the following natural generalization of Binary Search: in a given undirected, positively weighted graph, one vertex is a target. The algorithm's task is to identify the target by adaptively querying vertices. In response to querying a node $q$, the algorithm learns either that $q$ is the target, or is given an edge out of $q$ that lies on a shortest path from $q$ to the target. We study this problem in a general noisy model in which each query independently receives a correct answer with probability $p > \frac{1}{2}$ (a known constant), and an (adversarial) incorrect one with probability $1-p$. #R##N#Our main positive result is that when $p = 1$ (i.e., all answers are correct), $\log_2 n$ queries are always sufficient. For general $p$, we give an (almost information-theoretically optimal) algorithm that uses, in expectation, no more than $(1 - \delta)\frac{\log_2 n}{1 - H(p)} + o(\log n) + O(\log^2 (1/\delta))$ queries, and identifies the target correctly with probability at leas $1-\delta$. Here, $H(p) = -(p \log p + (1-p) \log(1-p))$ denotes the entropy. The first bound is achieved by the algorithm that iteratively queries a 1-median of the nodes not ruled out yet; the second bound by careful repeated invocations of a multiplicative weights algorithm. #R##N#Even for $p = 1$, we show several hardness results for the problem of determining whether a target can be found using $K$ queries. Our upper bound of $\log_2 n$ implies a quasipolynomial-time algorithm for undirected connected graphs; we show that this is best-possible under the Strong Exponential Time Hypothesis (SETH). Furthermore, for directed graphs, or for undirected graphs with non-uniform node querying costs, the problem is PSPACE-complete. For a semi-adaptive version, in which one may query $r$ nodes each in $k$ rounds, we show membership in $\Sigma_{2k-1}$ in the polynomial hierarchy, and hardness for $\Sigma_{2k-5}$.

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 "the complexity of interactively learning a stable matching by trial and error"):
  with members:
    - "a general framework for robust interactive learning"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "inapproximability of nash equilibrium"
    - "a stable marriage requires communication"
    - "the complexity of non monotone markets"
    - "settling the complexity of arrow debreu equilibria in markets with additively separable utilities"

Connected through hyperedge HE-3 (induced by source paper "searching trees with permanently noisy advice walking and query algorithms"):
  with members:
    - "binary search in graphs revisited"
    - "a general framework for robust interactive learning"
    - "submodular optimization under noise"
    - "noisy sorting without resampling"

Connected through hyperedge HE-4 (induced by source paper "noisy source location on a line"):
  with members:
    - "an adaptivity hierarchy theorem for property testing"
    - "the capacity of adaptive group testing"
    - "locating the source of diffusion in large scale networks"

Connected through hyperedge HE-5 (induced by source paper "competitive online search trees on trees"):
  with members:
    - "weighted dynamic finger in binary search trees"
    - "on the tree search problem with non uniform costs"
    - "self adjusting binary search trees what makes them tick"
    - "the power and limitations of static binary search trees with lazy finger"
    - "in pursuit of the dynamic optimality conjecture"
    - "searching in dynamic tree like partial orders"

Connected through hyperedge HE-6 (induced by source paper "stochastic submodular cover with limited adaptivity"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "stochastic packing integer programs with few queries"
    - "scenario submodular cover"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "parallel algorithms for select and partition with noisy comparisons"

Connected through hyperedge HE-7 (induced by source paper "a framework for searching in graphs in the presence of errors"):
  with members:
    - "binary search in graphs revisited"
    - "approximation strategies for generalized binary search in weighted trees"
    - "a general framework for robust interactive learning"
    - "on the tree search problem with non uniform costs"
    - "local algorithms for interactive clustering"
    - "query chains learning to rank from implicit feedback"

Connected through hyperedge HE-8 (induced by source paper "adaptive hierarchical clustering using ordinal queries"):
  with members:
    - "interactive bayesian hierarchical clustering"
    - "a cost function for similarity based hierarchical clustering"
    - "local algorithms for interactive clustering"
    - "active ranking using pairwise comparisons"
    - "adaptively learning the crowd kernel"
    - "learning multi modal similarity"
    - "noisy sorting without resampling"

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)