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: "large scale convex optimization for dense wireless cooperative networks"
  Abstract: Convex optimization is a powerful tool for resource allocation and signal processing in wireless networks. As the network density is expected to drastically increase in order to accommodate the exponentially growing mobile data traffic, performance optimization problems are entering a new era characterized by a high dimension and/or a large number of constraints, which poses significant design and computational challenges. In this paper, we present a novel two-stage approach to solve large-scale convex optimization problems for dense wireless cooperative networks, which can effectively detect infeasibility and enjoy modeling flexibility. In the proposed approach, the original large-scale convex problem is transformed into a standard cone programming form in the first stage via matrix stuffing, which only needs to copy the problem parameters such as channel state information (CSI) and quality-of-service (QoS) requirements to the prestored structure of the standard form. The capability of yielding infeasibility certificates and enabling parallel computing is achieved by solving the homogeneous self-dual embedding of the primal-dual pair of the standard form. In the solving stage, the operator splitting method, namely, the alternating direction method of multipliers (ADMM), is adopted to solve the large-scale homogeneous self-dual embedding. Compared with second-order methods, ADMM can solve large-scale problems in parallel with modest accuracy within a reasonable amount of time. Simulation results will demonstrate the speedup, scalability, and reliability of the proposed framework compared with the state-of-the-art modeling frameworks and solvers.

Connected through hyperedge HE-1 (induced by source paper "large scale beamforming for massive mimo via randomized sketching"):
  with members:
    - "generalized sparse and low rank optimization for ultra dense networks"
    - "optimal approximate matrix product in terms of stable rank"
    - "sketching as a tool for numerical linear algebra"
    - "uniform sampling for matrix approximation"
    - "single pass spectral sparsification in dynamic streams"
    - "group sparse beamforming for green cloud ran"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"

Connected through hyperedge HE-2 (induced by source paper "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff"):
  with members:
    - "energy sustainable iot with individual qos constraints through miso swipt multicasting"
    - "sparse representation for wireless communications"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "compressive channel estimation and multi user detection in c ran"
    - "a random access protocol for pilot allocation in crowded massive mimo systems"

Connected through hyperedge HE-3 (induced by source paper "generalized low rank optimization for topological cooperation in ultra dense networks"):
  with members:
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "matrix completion has no spurious local minimum"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "generalized degrees of freedom of the symmetric k user interference channel under finite precision csit"
    - "feasibility of interference alignment for the mimo interference channel"
    - "topological interference management with transmitter cooperation"
    - "topological interference management through index coding"

Connected through hyperedge HE-4 (induced by source paper "data shuffling in wireless distributed computing via low rank optimization"):
  with members:
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "a scalable framework for wireless distributed computing"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "feasibility of interference alignment for the mimo interference channel"

Connected through hyperedge HE-5 (induced by source paper "energy efficient massive mimo through distributed precoder design"):
  with members:
    - "joint user scheduling and beam selection optimization for beam based massive mimo downlinks"
    - "harvest the potential of massive mimo with multi layer techniques"
    - "green 5g heterogeneous networks through dynamic small cell operation"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "massive mimo ten myths and one critical question"
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "massive mimo meets hetnet interference coordination through spatial blanking"
    - "distributed robust multicell coordinated beamforming with imperfect csi an admm approach"

Connected through hyperedge HE-6 (induced by source paper "algorithm hardware co optimization of the memristor based framework for solving socp and homogeneous qcqp problems"):
  with members:
    - "optimal spectrum sharing in mimo cognitive radio networks via semidefinite programming"

Connected through hyperedge HE-7 (induced by source paper "a survey on mobile edge computing the communication perspective"):
  with members:
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "joint energy minimization and resource allocation in c ran with mobile cloud"
    - "latency optimization for resource allocation in mobile edge computation offloading"
    - "a survey on mobile edge networks convergence of computing caching and communications"
    - "proactive edge computing in latency constrained fog networks"
    - "computation peer offloading for energy constrained mobile edge computing in small cell networks"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "online placement of multi component applications in edge computing environments"

Connected through hyperedge HE-8 (induced by source paper "enhanced group sparse beamforming for green cloud ran a random matrix approach"):
  with members:
    - "layered group sparse beamforming for cache enabled green wireless networks"
    - "energy efficiency of downlink transmission strategies for cloud radio access networks"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "wireless communications in the era of big data"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "heterogeneous cellular networks using wireless backhaul fast admission control and large system analysis"
    - "robust group sparse beamforming for multicast green cloud ran with imperfect csi"
    - "are we approaching the fundamental limits of wireless network densification"

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.IT(Information Theory)