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: "on the approximation of submodular functions"
  Abstract: Submodular functions are a fundamental object of study in combinatorial optimization, economics, machine learning, etc. and exhibit a rich combinatorial structure. Many subclasses of submodular functions have also been well studied and these subclasses widely vary in their complexity. Our motivation is to understand the relative complexity of these classes of functions. Towards this, we consider the question of how well can one class of submodular functions be approximated by another (simpler) class of submodular functions. Such approximations naturally allow algorithms designed for the simpler class to be applied to the bigger class of functions. We prove both upper and lower bounds on such approximations. #R##N#Our main results are: #R##N#1. General submodular functions can be approximated by cut functions of directed graphs to a factor of $n^2/4$, which is tight. #R##N#2. General symmetric submodular functions$^{1}$ can be approximated by cut functions of undirected graphs to a factor of $n-1$, which is tight up to a constant. #R##N#3. Budgeted additive functions can be approximated by coverage functions to a factor of $e/(e-1)$, which is tight. #R##N#Here $n$ is the size of the ground set on which the submodular function is defined. #R##N#We also observe that prior works imply that monotone submodular functions can be approximated by coverage functions with a factor between $O(\sqrt{n} \log n)$ and $\Omega(n^{1/3} /\log^2 n) $.

Connected through hyperedge HE-1 (induced by source paper "the complexity of boolean surjective general valued csps"):
  with members:
    - "a dichotomy theorem for nonuniform csps"
    - "the proof of csp dichotomy conjecture"
    - "the complexity of finite valued csps"
    - "a galois connection for weighted relational clones of infinite size"
    - "algebraic properties of valued constraint satisfaction problem"
    - "the complexity of general valued csps"
    - "an algebraic hardness criterion for surjective constraint satisfaction"
    - "an algebraic theory of complexity for discrete optimization"

Connected through hyperedge HE-2 (induced by source paper "inhomogoenous hypergraph clustering with applications"):
  with members:
    - "on spectral partitioning of signed graphs"
    - "higher order organization of complex networks"
    - "estimation of kl divergence optimal minimax rate"
    - "scalable motif aware graph clustering"
    - "motif clustering and overlapping clustering for social network analysis"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "learning mixtures of ranking models"
    - "maximum likelihood estimation of functionals of discrete distributions"

Connected through hyperedge HE-3 (induced by source paper "maximizing symmetric submodular functions"):
  with members:
    - "constrained submodular maximization via a non symmetric technique"
    - "constrained submodular maximization beyond 1 e"
    - "improved approximation algorithms for k submodular function maximization"
    - "deterministic algorithms for submodular maximization problems"
    - "local distribution and the symmetry gap approximability of multiway partitioning problems"
    - "symmetry and approximability of submodular maximization problems"
    - "approximation algorithms for submodular multiway partition"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"

Connected through hyperedge HE-4 (induced by source paper "curvature and optimal algorithms for learning and minimizing submodular functions"):
  with members:
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "fast semidifferential based submodular function optimization"
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "learning mixtures of submodular shells with application to document summarization"
    - "near optimal nonmyopic value of information in graphical models"
    - "learning valuation functions"
    - "submodular functions learnability structure and optimization"
    - "symmetric submodular function minimization under hereditary family constraints"

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)