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: "a data prism semi verified learning in the small alpha regime"
  Abstract: We consider a model of unreliable or crowdsourced data where there is an underlying set of $n$ binary variables, each evaluator contributes a (possibly unreliable or adversarial) estimate of the values of some subset of $r$ of the variables, and the learner is given the true value of a constant number of variables. We show that, provided an $\alpha$-fraction of the evaluators are "good" (either correct, or with independent noise rate $p   1/(2-2p)^r$. This setting can be viewed as an instance of the semi-verified learning model introduced in [CSV17], which explores the tradeoff between the number of items evaluated by each worker and the fraction of good evaluators. Our results require the number of evaluators to be extremely large, $>n^r$, although our algorithm runs in linear time, $O_{r,\epsilon}(n)$, given query access to the large dataset of evaluations. This setting and results can also be viewed as examining a general class of semi-adversarial CSPs with a planted assignment. #R##N#This parameter regime where the fraction of reliable data is small, is relevant to a number of practical settings. For example, settings where one has a large dataset of customer preferences, with each customer specifying preferences for a small (constant) number of items, and the goal is to ascertain the preferences of a specific demographic of interest. Our results show that this large dataset (which lacks demographic information) can be leveraged together with the preferences of the demographic of interest for a constant number of randomly selected items, to recover an accurate estimate of the entire set of preferences. In this sense, our results can be viewed as a "data prism" allowing one to extract the behavior of specific cohorts from a large, mixed, dataset.

Connected through hyperedge HE-1 (induced by source paper "sever a robust meta algorithm for stochastic optimization"):
  with members:
    - "efficient algorithms and lower bounds for robust linear regression"
    - "robust physical world attacks on deep learning models"
    - "certified defenses for data poisoning attacks"
    - "learning geometric concepts with nasty noise"
    - "outlier robust moment estimation via sum of squares"
    - "being robust in high dimensions can be practical"
    - "meta qsar a large scale application of meta learning to drug design and discovery"
    - "the consciousness prior"

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.LG(Machine Learning)