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: "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.

Connected through hyperedge HE-1 (induced by source paper "how bad is worst case data if you know where it comes from"):
  with members:
    - "regression from dependent observations"
    - "list decodable linear regression"
    - "a theory of selective prediction"
    - "efficient algorithms for outlier robust regression"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "learning from untrusted data"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"

Connected through hyperedge HE-2 (induced by source paper "a theory of selective prediction"):
  with members:
    - "estimating learnability in the sublinear data regime"
    - "prediction with a short memory"
    - "chasing ghosts competing with stateful policies"

Connected through hyperedge HE-3 (induced by source paper "prediction of infinite words with automata"):
  with members:
    - "is there an elegant universal theory of prediction"

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)