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: "ranking with submodular valuations"
  Abstract: We study the problem of ranking with submodular valuations. An instance of this problem consists of a ground set $[m]$, and a collection of $n$ monotone submodular set functions $f^1, \ldots, f^n$, where each $f^i: 2^{[m]} \to R_+$. An additional ingredient of the input is a weight vector $w \in R_+^n$. The objective is to find a linear ordering of the ground set elements that minimizes the weighted cover time of the functions. The cover time of a function is the minimal number of elements in the prefix of the linear ordering that form a set whose corresponding function value is greater than a unit threshold value. #R##N#Our main contribution is an $O(\ln(1 / \epsilon))$-approximation algorithm for the problem, where $\epsilon$ is the smallest non-zero marginal value that any function may gain from some element. Our algorithm orders the elements using an adaptive residual updates scheme, which may be of independent interest. We also prove that the problem is $\Omega(\ln(1 / \epsilon))$-hard to approximate, unless P = NP. This implies that the outcome of our algorithm is optimal up to constant factors.

Connected through hyperedge HE-1 (induced by source paper "the online min sum set cover problem"):
  with members:
    - "chasing convex bodies optimally"
    - "stochastic online metric matching"
    - "pure entropic regularization for metrical task systems"
    - "metrical task systems on trees via mirror descent and unfair gluing"
    - "weighted k server bounds via combinatorial dichotomies"
    - "competitive algorithms for generalized k server in uniform metrics"
    - "a static optimality transformation with applications to planar point location"
    - "the generalized work function algorithm is competitive for the generalized 2 server problem"

Connected through hyperedge HE-2 (induced by source paper "sequential evaluation and generation framework for combinatorial recommender system"):
  with members:
    - "deep reinforcement learning for page wise recommendations"
    - "off policy deep reinforcement learning without exploration"
    - "scalable agent alignment via reward modeling a research direction"
    - "virtual taobao virtualizing real world online retail environment for reinforcement learning"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "attention is all you need"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "learning combinatorial optimization algorithms over graphs"

Connected through hyperedge HE-3 (induced by source paper "learning optimal search algorithms from data"):
  with members:
    - "learning to prune speeding up repeated computations"
    - "the markovian price of information"
    - "learning to branch"
    - "competitive caching with machine learned advice"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "the price of information in combinatorial optimization"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "adaptivity gaps for stochastic probing submodular and xos functions"

Connected through hyperedge HE-4 (induced by source paper "stochastic submodular cover with limited adaptivity"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "stochastic packing integer programs with few queries"
    - "scenario submodular cover"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "parallel algorithms for select and partition with noisy comparisons"

Connected through hyperedge HE-5 (induced by source paper "adaptive submodular ranking"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "scenario submodular cover"
    - "near optimal bayesian active learning for decision making"
    - "submodular meets structured finding diverse subsets in exponentially large structured item sets"
    - "analytical approach to parallel repetition"
    - "a note on the generalized min sum set cover problem"
    - "near optimal bayesian active learning with noisy observations"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

Connected through hyperedge HE-6 (induced by source paper "competitive algorithms from competitive equilibria non clairvoyant scheduling under polyhedral constraints"):
  with members:
    - "selfishmigrate a scalable algorithm for non clairvoyantly scheduling heterogeneous processors"
    - "online primal dual for non linear optimization with applications to speed scaling"
    - "minimum latency submodular cover"
    - "a truthful mechanism for offline ad slot scheduling"

Connected through hyperedge HE-7 (induced by source paper "ranking with diverse intents and correlated contents"):
  with members:
    - "approximation algorithms for maximum independent set of pseudo disks"
    - "a note on the generalized min sum set cover problem"
    - "minimum latency submodular cover"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "dependent randomized rounding for matroid polytopes and applications"
    - "a unified approach to ranking in probabilistic databases"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-8 (induced by source paper "precoder design for physical layer multicasting"):
  with members:
    - "efficient submodular function maximization under linear packing constraints"
    - "approximation analysis of influence spread in social networks"
    - "on k column sparse packing programs"

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)