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 approximation sampling based algorithms and lower bounds"
  Abstract: We introduce several generalizations of classical computer science problems obtained by replacing simpler objective functions with general submodular functions. The new problems include submodular load balancing, which generalizes load balancing or minimum-makespan scheduling, submodular sparsest cut and submodular balanced cut, which generalize their respective graph cut problems, as well as submodular function minimization with a cardinality lower bound. We establish upper and lower bounds for the approximability of these problems with a polynomial number of queries to a function-value oracle. The approximation guarantees for most of our algorithms are of the order of sqrt(n/ln n). We show that this is the inherent difficulty of the problems by proving matching lower bounds. We also give an improved lower bound for the problem of approximately learning a monotone submodular function. In addition, we present an algorithm for approximately learning submodular functions with special structure, whose guarantee is close to the lower bound. Although quite restrictive, the class of functions with this structure includes the ones that are used for lower bounds both by us and in previous work. This demonstrates that if there are significantly stronger lower bounds for this problem, they rely on more general submodular functions.

Connected through hyperedge HE-1 (induced by source paper "optimal approximation for unconstrained non submodular minimization"):
  with members:
    - "near optimal approximate discrete and continuous submodular function minimization"
    - "fast maximization of non submodular monotonic functions on the integer lattice"
    - "on maximization of weakly modular functions guarantees of multi stage algorithms tractability and hardness"
    - "gradient methods for submodular maximization"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "submodular optimization under noise"
    - "subquadratic submodular function minimization"
    - "tolerant junta testing and the connection to submodular optimization and function isomorphism"

Connected through hyperedge HE-2 (induced by source paper "a unified framework of robust submodular optimization"):
  with members:
    - "near optimal algorithms for hard submodular programs with discounted cooperative costs"
    - "learning from less data a unified data subset selection and active learning framework for computer vision"
    - "robust monotone submodular function maximization"
    - "distributionally robust submodular maximization"
    - "approximating some network problems with scenarios"
    - "a framework towards domain specific video summarization"
    - "robust optimization for non convex objectives"
    - "robust submodular maximization offline and online algorithms"

Connected through hyperedge HE-3 (induced by source paper "beyond submodular maximization"):
  with members:
    - "distributed maximization of submodular plus diversity functions for multi label feature selection on huge datasets"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "local search for max sum diversification"
    - "max sum diversification monotone submodular functions and semi metric spaces"
    - "max sum diversity via convex programming"
    - "building a good team secretary problems and the supermodular degree"

Connected through hyperedge HE-4 (induced by source paper "approximating nash social welfare under submodular valuations through un matchings"):
  with members:
    - "on fair division of indivisible items"
    - "a tale of santa claus hypergraphs and matroids"
    - "approximating the nash social welfare with budget additive valuations"
    - "finding fair and efficient allocations"
    - "convex program duality fisher markets and nash social welfare"
    - "nash social welfare for indivisible items under separable piecewise linear concave utilities"
    - "nash social welfare matrix permanent and stable polynomials"
    - "apx hardness of maximizing nash social welfare with indivisible items"

Connected through hyperedge HE-5 (induced by source paper "majorisation minimisation algorithms for minimising the difference between lattice submodular functions"):
  with members:
    - "continuous dr submodular maximization structure and algorithms"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "submodular functions from discrete to continous domains"
    - "submodular function maximization on the bounded integer lattice"
    - "maximizing monotone submodular functions over the integer lattice"
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "a submodular supermodular procedure with applications to discriminative structure learning"
    - "learning mixtures of submodular shells with application to document summarization"

Connected through hyperedge HE-6 (induced by source paper "subadditive load balancing"):
  with members:
    - "mixed robust average submodular partitioning fast algorithms guarantees and applications"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "learning with submodular functions a convex optimization perspective"
    - "efficient minimization of decomposable submodular functions"
    - "submodular functions learnability structure and optimization"

Connected through hyperedge HE-7 (induced by source paper "a faster fptas for knapsack problem with cardinality constraint"):
  with members:
    - "a faster fptas for the unbounded knapsack problem"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "optimal source based filtering of malicious traffic"
    - "bin packing with general cost structures"
    - "hybrid rounding techniques for knapsack problems"

Connected through hyperedge HE-8 (induced by source paper "sample greedy based task allocation for multiple robot systems"):
  with members:
    - "deep submodular functions"
    - "fast non monotone submodular maximisation subject to a matroid constraint"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "weakly supervised discovery of visual pattern configurations"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"

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)