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: "distribution independent reliable learning"
  Abstract: We study several questions in the reliable agnostic learning framework of Kalai et al. (2009), which captures learning tasks in which one type of error is costlier than others. A positive reliable classifier is one that makes no false positive errors. The goal in the positive reliable agnostic framework is to output a hypothesis with the following properties: (i) its false positive error rate is at most $\epsilon$, (ii) its false negative error rate is at most $\epsilon$ more than that of the best positive reliable classifier from the class. A closely related notion is fully reliable agnostic learning, which considers partial classifiers that are allowed to predict "unknown" on some inputs. The best fully reliable partial classifier is one that makes no errors and minimizes the probability of predicting "unknown", and the goal in fully reliable learning is to output a hypothesis that is almost as good as the best fully reliable partial classifier from a class. #R##N#For distribution-independent learning, the best known algorithms for PAC learning typically utilize polynomial threshold representations, while the state of the art agnostic learning algorithms use point-wise polynomial approximations. We show that one-sided polynomial approximations, an intermediate notion between polynomial threshold representations and point-wise polynomial approximations, suffice for learning in the reliable agnostic settings. We then show that majorities can be fully reliably learned and disjunctions of majorities can be positive reliably learned, through constructions of appropriate one-sided polynomial approximations. Our fully reliable algorithm for majorities provides the first evidence that fully reliable learning may be strictly easier than agnostic learning. Our algorithms also satisfy strong attribute-efficiency properties, and provide smooth tradeoffs between sample complexity and running time.

Connected through hyperedge HE-1 (induced by source paper "a nearly optimal lower bound on the approximate degree of ac 0"):
  with members:
    - "on szk and pp"
    - "separations in query complexity based on pointer functions"
    - "dual polynomials for collision and element distinctness"
    - "dual lower bounds for approximate degree and markov bernstein inequalities"
    - "faster private release of marginals on small databases"
    - "hardness amplification and the approximate degree of constant depth circuits"
    - "faster algorithms for privately releasing marginals"
    - "a separation of np and conp in multiparty communication complexity"

Connected through hyperedge HE-2 (induced by source paper "the extended littlestone s dimension for learning with mistakes and abstentions"):
  with members:
    - "learning to abstain from binary prediction"
    - "beyond disagreement based agnostic active learning"
    - "online learning via sequential complexities"
    - "a tutorial on conformal prediction"

Connected through hyperedge HE-3 (induced by source paper "conditional sparse linear regression"):
  with members:
    - "the optimal sample complexity of pac learning"

Connected through hyperedge HE-4 (induced by source paper "dual polynomials for collision and element distinctness"):
  with members:
    - "dual lower bounds for approximate degree and markov bernstein inequalities"
    - "faster private release of marginals on small databases"
    - "a note on the quantum collision and set equality problems"
    - "hardness amplification and the approximate degree of constant depth circuits"
    - "a quantum lower bound for distinguishing random functions from random permutations"
    - "faster algorithms for privately releasing marginals"
    - "strong direct product theorems for quantum communication and query complexity"
    - "the intersection of two halfspaces has high threshold degree"

Connected through hyperedge HE-5 (induced by source paper "active learning from weak and strong labelers"):
  with members:
    - "learning from data with heterogeneous noise using sgd"
    - "beyond disagreement based agnostic active learning"
    - "active and passive learning of linear separators under log concave distributions"
    - "agnostic active learning without constraints"

Connected through hyperedge HE-6 (induced by source paper "hardness amplification and the approximate degree of constant depth circuits"):
  with members:
    - "dual lower bounds for approximate degree and markov bernstein inequalities"
    - "on arthur merlin games in communication complexity"
    - "a separation of np and conp in multiparty communication complexity"
    - "strong direct product theorems for quantum communication and query complexity"
    - "the intersection of two halfspaces has high threshold degree"
    - "a note on the sign degree of formulas"
    - "optimal bounds for sign representing the intersection of two halfspaces by polynomials"
    - "multiparty communication complexity of disjointness"

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)