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: "submodular maximization beyond non negativity guarantees fast algorithms and applications"
  Abstract: It is generally believed that submodular functions -- and the more general class of $\gamma$-weakly submodular functions -- may only be optimized under the non-negativity assumption $f(S) \geq 0$. In this paper, we show that once the function is expressed as the difference $f = g - c$, where $g$ is monotone, non-negative, and $\gamma$-weakly submodular and $c$ is non-negative modular, then strong approximation guarantees may be obtained. We present an algorithm for maximizing $g - c$ under a $k$-cardinality constraint which produces a random feasible set $S$ such that $\mathbb{E} \left[ g(S) - c(S) \right] \geq (1 - e^{-\gamma} - \epsilon) g(OPT) - c(OPT)$, whose running time is $O (\frac{n}{\epsilon} \log^2 \frac{1}{\epsilon})$, i.e., independent of $k$. We extend these results to the unconstrained setting by describing an algorithm with the same approximation guarantees and faster $O(\frac{n}{\epsilon} \log\frac{1}{\epsilon})$ runtime. The main techniques underlying our algorithms are two-fold: the use of a surrogate objective which varies the relative importance between $g$ and $c$ throughout the algorithm, and a geometric sweep over possible $\gamma$ values. Our algorithmic guarantees are complemented by a hardness result showing that no polynomial-time algorithm which accesses $g$ through a value oracle can do better. We empirically demonstrate the success of our algorithms by applying them to experimental design on the Boston Housing dataset and directed vertex cover on the Email EU dataset.

Connected through hyperedge HE-1 (induced by source paper "regularized submodular maximization at scale"):
  with members:
    - "flexible modeling of diversity with strongly log concave distributions"
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "strong log concavity does not imply log submodularity"
    - "an optimal streaming algorithm for non monotone submodular maximization"
    - "making a sieve random improved semi streaming algorithm for submodular maximization under a cardinality constraint"
    - "data summarization at scale a two stage submodular approach"
    - "a tight approximation for submodular maximization with mixed packing and covering constraints"
    - "do less get more streaming submodular maximization with subsampling"

Connected through hyperedge HE-2 (induced by source paper "pandora s box problem with order constraints"):
  with members:
    - "near optimal adaptivity gaps for stochastic multi value probing"
    - "pandora s problem with nonobligatory inspection"
    - "generalizing complex hypotheses on product distributions auctions prophet inequalities and pandora s problem"
    - "guess free maximization of submodular and linear sums"
    - "the price of information in combinatorial optimization"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"

Connected through hyperedge HE-3 (induced by source paper "a note on maximizing the difference between a monotone submodular function and a linear function"):
  with members:
    - "guess free maximization of submodular and linear sums"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"

Connected through hyperedge HE-4 (induced by source paper "guarantees of stochastic greedy algorithms for non monotone submodular maximization with cardinality constraint"):
  with members:
    - "interlaced greedy algorithm for maximization of submodular functions in nearly linear time"
    - "a memoization framework for scaling submodular optimization to large scale problems"
    - "non monotone submodular maximization with nearly optimal adaptivity and query complexity"
    - "stochastic greedy closing the optimality gap in exact weak submodular maximization"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "greed is good near optimal submodular maximization via greedy optimization"

Connected through hyperedge HE-5 (induced by source paper "regression under human assistance"):
  with members:
    - "adaptive hard thresholding for near optimal consistent robust regression"
    - "selectivenet a deep neural network with an integrated reject option"
    - "the algorithmic automation problem prediction triage and human effort"
    - "direct uncertainty prediction for medical second opinions"
    - "non submodular function maximization subject to a matroid constraint with applications"
    - "bias reduced uncertainty estimation for deep neural classifiers"
    - "active regression via linear sample sparsification"
    - "fasttext zip compressing text classification models"

Connected through hyperedge HE-6 (induced by source paper "flexible modeling of diversity with strongly log concave distributions"):
  with members:
    - "fair and diverse dpp based data summarization"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "gdpp learning diverse generations using determinantal point process"
    - "guess free maximization of submodular and linear sums"
    - "unbiased estimates for linear regression via volume sampling"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "kronecker determinantal point processes"
    - "diversity networks neural network compression using determinantal point processes"

Connected through hyperedge HE-7 (induced by source paper "implementation in advised strategies welfare guarantees from posted price mechanisms when demand queries are np hard"):
  with members:
    - "guess free maximization of submodular and linear sums"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "breaking the logarithmic barrier for truthful combinatorial auctions with submodular bidders"
    - "computational efficiency requires simple taxation"
    - "inapproximability of truthful mechanisms via generalizations of the vc dimension"
    - "combinatorial auctions via posted prices"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "the computational complexity of truthfulness in combinatorial auctions"

Connected through hyperedge HE-8 (induced by source paper "optimization of chance constrained submodular functions"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "evolutionary algorithms for the chance constrained knapsack problem"
    - "pareto optimization for subset selection with dynamic cost constraints"
    - "greedy maximization of functions with bounded curvature under partition matroid constraints"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "social dynamics of digg"

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)