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: "estimating bernoulli trial probability from a small sample"
  Abstract: AbstractThe standard textbook method for estimating the probability of a biased coin from ﬁnite tosses implicitlyassumes the sample sizes are large and gives incorrect results for small samples. We describe the exact solution,which is correct for any sample size. Keywords: estimation of population mean, sampling without replacement, conﬁdence interval, coin-tossing prob-lems, regularized incomplete beta function, Bernoulli process 1 Introduction Consider the following problem. A biased coin, with an unknown probability pof heads, is tossed ntimes, andmheads result. What is the best estimate of pfrom nand m?Problems of this form occur in many applications. A typical example is found in Ref. [1, p. 346]. Theirproblem is to determine the percentage of Democratic votes, and the conﬁdence interval for that percentage,given that 917 voters in a sample of 1,600 (out of 25,000) are Democrats.The solution given in the book is as follows. The observed ratio 9171,600 ≈ 0.57 is the estimate for the fraction ofDemocratic voters. The standard deviation (SD) is estimated by a bootstrap procedure as√0.57 · 0.43 ≈ 0.50.The standard error (SE) is computed as√1,600 · SD ≈ 20, or 1.25%. The 95% conﬁdence interval is estimatedas 57 ± 2 · 1.25%.This method, which we will call the “standard method,” does not work when samples sizes are small or thefraction is near the extremes of 0 or 1. For example, suppose an urn is ﬁlled with marbles, an unknown fractionof which are red and the rest white. A sample of 5 marbles is taken (with replacement), and in that sampleall marbles are white. What is the fraction and 80% conﬁdence interval of red marbles? The standard methodyields a fraction of zero and a conﬁdence interval of zero. These are obviously wrong. A sample of 5 whitesindicates that the fraction of reds is probably small, but it certainly provides no assurance that it is zero; instead,it is very likely to be nonzero.We will describe another method, which we will call the “exact method,” that does not have these errors.There are many practical cases where small sample sizes are important. For example, a medical trial may involvejust a dozen or so patients. It can be useful to use the exact method for such studies. Indeed, in some casesit might be considered irresponsible not to use it, since the standard method could lead to incorrect decisionsbased on misleading results.This problem arose when the authors needed an estimate and conﬁdence interval for the probability of a rareevent that may occur less than a dozen times, or even never, in a sample of several billion. [2] After the standardmethod yielded obviously wrong results, the authors were surprised that a literature search did not yield theexact method. The purpose of this note is to document it for general use.The next section describes the assumptions and detailed derivation of the exact method. The reader whojust wants to see the ﬁnal result may refer to Eqs. (1), (2), and (3) below, which show the exact method’s mean,lower conﬁdence level, and upper conﬁdence level respectively.

Connected through hyperedge HE-1 (induced by source paper "long range indoor navigation with prm rl"):
  with members:
    - "learning navigation behaviors end to end with autorl"
    - "pearl preference appraisal reinforcement learning for motion planning"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "deepnav learning to navigate large cities"
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"

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.DC(Distributed, Parallel, and Cluster Computing)