human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "high confidence predictions under adversarial uncertainty"):
  Abstract: We study the setting in which the bits of an unknown infinite binary sequence x are revealed sequentially to an observer. We show that very limited assumptions about x allow one to make successful predictions about unseen bits of x. First, we study the problem of successfully predicting a single 0 from among the bits of x. In our model we have only one chance to make a prediction, but may do so at a time of our choosing. We describe and motivate this as the problem of a frog who wants to cross a road safely. #R##N#Letting N_t denote the number of 1s among the first t bits of x, we say that x is "eps-weakly sparse" if lim inf (N_t/t) <= eps. Our main result is a randomized algorithm that, given any eps-weakly sparse sequence x, predicts a 0 of x with success probability as close as desired to 1 - \eps. Thus we can perform this task with essentially the same success probability as under the much stronger assumption that each bit of x takes the value 1 independently with probability eps. We apply this result to show how to successfully predict a bit (0 or 1) under a broad class of possible assumptions on the sequence x. The assumptions are stated in terms of the behavior of a finite automaton M reading the bits of x. #R##N#We also propose and solve a variant of the well-studied "ignorant forecasting" problem. For every eps > 0, we give a randomized forecasting algorithm S_eps that, given sequential access to a binary sequence x, makes a prediction of the form: "A p fraction of the next N bits will be 1s." (The algorithm gets to choose p, N, and the time of the prediction.) For any fixed sequence x, the forecast fraction p is accurate to within +-eps with probability 1 - eps.

Includes members:
  Member "prediction strategies without loss":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "competitive ratio versus regret minimization achieving the best of both worlds")
  Member "dimension in complexity classes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "asymptotic divergences and strong dichotomy")
  Member "the dimensions of individual strings and sequences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "who asked us how the theory of computing answers questions about analysis")

Question: Please tell me which class the source paper 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)