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: "efficient submodular function maximization under linear packing constraints"
  Abstract: We study the problem of maximizing a monotone submodular set function subject to linear packing constraints. An instance of this problem consists of a matrix $A \in [0,1]^{m \times n}$, a vector $b \in [1,\infty)^m$, and a monotone submodular set function $f: 2^{[n]} \rightarrow \bbR_+$. The objective is to find a set $S$ that maximizes $f(S)$ subject to $A x_{S} \leq b$, where $x_S$ stands for the characteristic vector of the set $S$. A well-studied special case of this problem is when $f$ is linear. This special case captures the class of packing integer programs. #R##N#Our main contribution is an efficient combinatorial algorithm that achieves an approximation ratio of $\Omega(1 / m^{1/W})$, where $W = \min\{b_i / A_{ij} : A_{ij} > 0\}$ is the width of the packing constraints. This result matches the best known performance guarantee for the linear case. One immediate corollary of this result is that the algorithm under consideration achieves constant factor approximation when the number of constraints is constant or when the width of the constraints is sufficiently large. This motivates us to study the large width setting, trying to determine its exact approximability. We develop an algorithm that has an approximation ratio of $(1 - \epsilon)(1 - 1/e)$ when $W = \Omega(\ln m / \epsilon^2)$. This result essentially matches the theoretical lower bound of $1 - 1/e$. We also study the special setting in which the matrix $A$ is binary and $k$-column sparse. A $k$-column sparse matrix has at most $k$ non-zero entries in each of its column. We design a fast combinatorial algorithm that achieves an approximation ratio of $\Omega(1 / (Wk^{1/W}))$, that is, its performance guarantee only depends on the sparsity and width parameters.

Connected through hyperedge HE-1 (induced by source paper "costly circuits submodular schedules hybrid switch scheduling for data centers"):
  with members:
    - "adaptive policies for scheduling with reconfiguration delay an end to end solution for all optical data centers"

Connected through hyperedge HE-2 (induced by source paper "precoder design for physical layer multicasting"):
  with members:
    - "approximation analysis of influence spread in social networks"
    - "ranking with submodular valuations"
    - "on k column sparse packing programs"

Connected through hyperedge HE-3 (induced by source paper "multi user mimo scheduling in the fourth generation cellular uplink"):
  with members:
    - "multi user scheduling in the 3gpp lte cellular uplink"
    - "sub modularity and antenna selection in mimo systems"
    - "on k column sparse packing programs"
    - "limited feedback based block diagonalization for the mimo broadcast channel"
    - "message passing resource allocation for the uplink of multicarrier systems"
    - "joint beamforming for multiaccess mimo systems with finite rate feedback"
    - "a tight lower bound to the outage probability of discrete input block fading channels"
    - "multiuser mimo achievable rates with downlink training and channel state feedback"

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)