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: "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
  Abstract: In this paper we study the fundamental problems of maximizing a continuous non-monotone submodular function over the hypercube, both with and without coordinate-wise concavity. This family of optimization problems has several applications in machine learning, economics, and communication systems. Our main result is the first $\frac{1}{2}$-approximation algorithm for continuous submodular function maximization; this approximation factor of $\frac{1}{2}$ is the best possible for algorithms that only query the objective function at polynomially many points. For the special case of DR-submodular maximization, i.e. when the submodular functions is also coordinate wise concave along all coordinates, we provide a different $\frac{1}{2}$-approximation algorithm that runs in quasilinear time. Both of these results improve upon prior work [Bian et al, 2017, Soma and Yoshida, 2017]. #R##N#Our first algorithm uses novel ideas such as reducing the guaranteed approximation problem to analyzing a zero-sum game for each coordinate, and incorporates the geometry of this zero-sum game to fix the value at this coordinate. Our second algorithm exploits coordinate-wise concavity to identify a monotone equilibrium condition sufficient for getting the required approximation guarantee, and hunts for the equilibrium point using binary search. We further run experiments to verify the performance of our proposed algorithms in related machine learning applications.

Connected through hyperedge HE-1 (induced by source paper "online non monotone dr submodular maximization"):
  with members:
    - "introduction to online convex optimization"
    - "an optimal algorithm for online unconstrained submodular maximization"
    - "non monotone continuous dr submodular maximization structure and algorithms"
    - "gradient methods for submodular maximization"
    - "robust budget allocation via continuous submodular functions"
    - "potential function proofs for first order methods"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "the power of randomization distributed submodular maximization on massive datasets"

Connected through hyperedge HE-2 (induced by source paper "non monotone dr submodular maximization approximation and regret guarantees"):
  with members:
    - "introduction to online convex optimization"
    - "an optimal algorithm for online unconstrained submodular maximization"
    - "non monotone continuous dr submodular maximization structure and algorithms"
    - "gradient methods for submodular maximization"
    - "robust budget allocation via continuous submodular functions"
    - "non monotone dr submodular function maximization"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "submodular functions from discrete to continous domains"

Connected through hyperedge HE-3 (induced by source paper "combinatorial auctions with interdependent valuations sos to the rescue"):
  with members:
    - "interdependent values without single crossing"
    - "non monotone continuous dr submodular maximization structure and algorithms"
    - "approximate revenue maximization in interdependent value settings"
    - "optimal mechanisms for selling information"
    - "optimal auctions with correlated bidders are easy"

Connected through hyperedge HE-4 (induced by source paper "unconstrained submodular maximization with constant adaptive complexity"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "do less get more streaming submodular maximization with subsampling"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "gradient methods for submodular maximization"
    - "greed is good near optimal submodular maximization via greedy optimization"

Connected through hyperedge HE-5 (induced by source paper "submodular maximization with matroid and packing constraints in parallel"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "constrained submodular maximization via a non symmetric technique"

Connected through hyperedge HE-6 (induced by source paper "optimal dr submodular maximization and applications to provable mean field inference"):
  with members:
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "robust budget allocation via continuous submodular functions"
    - "non monotone dr submodular function maximization"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "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"
    - "scalable variational inference in log supermodular models"

Connected through hyperedge HE-7 (induced by source paper "maximizing monotone dr submodular continuous functions by derivative free optimization"):
  with members:
    - "gradient methods for submodular maximization"
    - "continuous dr submodular maximization structure and algorithms"
    - "robust budget allocation via continuous submodular functions"
    - "submodular optimization under noise"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "submodular functions from discrete to continous domains"
    - "maximizing monotone submodular functions over the integer lattice"
    - "online submodular welfare maximization greedy is optimal"

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)