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: "agnostic learning of monomials by halfspaces is hard"
  Abstract: We prove the following strong hardness result for learning: Given a distribution of labeled examples from the hypercube such that there exists a monomial consistent with $(1-\eps)$ of the examples, it is NP-hard to find a halfspace that is correct on $(1/2+\eps)$ of the examples, for arbitrary constants $\eps > 0$. In learning theory terms, weak agnostic learning of monomials is hard, even if one is allowed to output a hypothesis from the much bigger concept class of halfspaces. This hardness result subsumes a long line of previous results, including two recent hardness results for the proper learning of monomials and halfspaces. As an immediate corollary of our result we show that weak agnostic learning of decision lists is NP-hard. #R##N#Our techniques are quite different from previous hardness proofs for learning. We define distributions on positive and negative examples for monomials whose first few moments match. We use the invariance principle to argue that regular halfspaces (all of whose coefficients have small absolute value relative to the total $\ell_2$ norm) cannot distinguish between distributions whose first few moments match. For highly non-regular subspaces, we use a structural lemma from recent work on fooling halfspaces to argue that they are ``junta-like'' and one can zero out all but the top few coefficients without affecting the performance of the halfspace. The top few coefficients form the natural list decoding of a halfspace in the context of dictatorship tests/Label Cover reductions. #R##N#We note that unlike previous invariance principle based proofs which are only known to give Unique-Games hardness, we are able to reduce from a version of Label Cover problem that is known to be NP-hard. This has inspired follow-up work on bypassing the Unique Games conjecture in some optimal geometric inapproximability results.

Connected through hyperedge HE-1 (induced by source paper "solving empirical risk minimization in the current matrix multiplication time"):
  with members:
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "solving linear programs in the current matrix multiplication time"
    - "on the local minima of the empirical risk"
    - "limits on the universal method for matrix multiplication"
    - "guaranteed sufficient decrease for variance reduced stochastic gradient descent"
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "variance reduced stochastic gradient descent with sufficient decrease"

Connected through hyperedge HE-2 (induced by source paper "equal opportunity in online classification with partial feedback"):
  with members:
    - "two player games for efficient non convex constrained optimization"
    - "a reductions approach to fair classification"
    - "calibrated fairness in bandits"
    - "runaway feedback loops in predictive policing"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "taming the monster a fast and simple algorithm for contextual bandits"

Connected through hyperedge HE-3 (induced by source paper "semi supervised ordinal regression based on empirical risk minimization"):
  with members:
    - "chainer a deep learning framework for accelerating the research cycle"
    - "classification from pairwise similarity and unlabeled data"
    - "positive unlabeled learning with non negative risk estimator"
    - "loss factorization weakly supervised learning and label noise robustness"
    - "semi supervised classification based on classification from positive and unlabeled data"
    - "theoretical comparisons of positive unlabeled learning against positive negative learning"
    - "on the consistency of ordinal regression methods"
    - "transductive ordinal regression"

Connected through hyperedge HE-4 (induced by source paper "learning only from relevant keywords and unlabeled documents"):
  with members:
    - "on the convergence of adam and beyond"
    - "bag of tricks for efficient text classification"
    - "character level convolutional networks for text classification"
    - "making risk minimization tolerant to label noise"
    - "adam a method for stochastic optimization"
    - "on the consistency of auc pairwise optimization"
    - "scikit learn machine learning in python"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-5 (induced by source paper "eliciting and enforcing subjective individual fairness"):
  with members:
    - "metric learning for individual fairness"
    - "online learning with an unknown fairness metric"
    - "probably approximately metric fair learning"
    - "how to use heuristics for differential privacy"
    - "fairness through computationally bounded awareness"
    - "a reductions approach to fair classification"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "fairness through awareness"

Connected through hyperedge HE-6 (induced by source paper "hardness of learning dnfs using halfspaces"):
  with members:
    - "learning convex polytopes with margin"
    - "hardness of learning noisy halfspaces using polynomial thresholds"
    - "analysis of boolean functions"
    - "learning dnf expressions from fourier spectrum"
    - "hardness results for agnostically learning low degree polynomial threshold functions"

Connected through hyperedge HE-7 (induced by source paper "tight approximation bounds for maximum multi coverage"):
  with members:
    - "algorithmic aspects of optimal channel coding"
    - "learning coverage functions and private release of marginals"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "limitations of randomized mechanisms for combinatorial auctions"

Connected through hyperedge HE-8 (induced by source paper "calibrated surrogate maximization of linear fractional utility in binary classification"):
  with members:
    - "on structured prediction theory with calibrated convex surrogate losses"
    - "beyond convexity stochastic quasi convex optimization"
    - "on the consistency of auc pairwise optimization"
    - "optimizing f measure a tale of two approaches"

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.CC(Computational Complexity)