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: "sparse signal processing concepts for efficient 5g system design"
  Abstract: As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges, and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper, we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will also describe applications of this sparse signal processing paradigm in Multiple Input Multiple Output random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize an important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.

Connected through hyperedge HE-1 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach"):
  with members:
    - "phase transition analysis for covariance based massive random access with massive mimo"
    - "the roadmap to 6g ai empowered wireless networks"
    - "improved scaling law for activity detection in massive mimo systems"
    - "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part ii achievable rate characterization"

Connected through hyperedge HE-2 (induced by source paper "simultaneous structures in convex signal recovery revisiting the convex combination of norms"):
  with members:
    - "sparse power factorization balancing peakiness and sample complexity"
    - "near optimal sample complexity for convex tensor completion"
    - "branchhull convex bilinear inversion from the entrywise product of signals with known signs"
    - "reliable recovery of hierarchically sparse signals and application in machine type communications"
    - "low rank tensor recovery via iterative hard thresholding"
    - "tensor theta norms and low rank recovery"
    - "self calibration and biconvex compressive sensing"
    - "convex recovery of a structured signal from independent random linear measurements"

Connected through hyperedge HE-3 (induced by source paper "multi cell sparse activity detection for massive random access massive mimo versus cooperative mimo"):
  with members:
    - "sparse activity detection for massive connectivity"
    - "a new scaling law for activity detection in massive mimo systems"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "a random access protocol for pilot allocation in crowded massive mimo systems"
    - "user activity detection in massive random access compressed sensing vs coded slotted aloha"
    - "device activity and embedded information bit detection using amp in massive mimo"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "capacity of gaussian many access channels"

Connected through hyperedge HE-4 (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-5 (induced by source paper "towards massive connectivity support for scalable mmtc communications in 5g networks"):
  with members:
    - "a random access protocol for pilot allocation in crowded massive mimo systems"
    - "compressive coded random access for massive mtc traffic in 5g systems"
    - "reliable and efficient access for alarm initiated and regular m2m traffic in ieee 802 11ah systems"
    - "massive machine type communication mmtc access with integrated authentication"
    - "reliable recovery of hierarchically sparse signals and application in machine type communications"
    - "pulse shaped ofdm for 5g systems"
    - "coded pilot access a random access solution for massive mimo systems"
    - "random access for massive mimo systems with intra cell pilot contamination"

Connected through hyperedge HE-6 (induced by source paper "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"):
  with members:
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "a random access protocol for pilot allocation in crowded massive mimo systems"
    - "compressive coded random access for massive mtc traffic in 5g systems"
    - "device activity and embedded information bit detection using amp in massive mimo"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "capacity of gaussian many access channels"

Connected through hyperedge HE-7 (induced by source paper "hierarchical restricted isometry property for kronecker product measurements"):
  with members:
    - "low overhead hierarchically sparse channel estimation for multiuser wideband massive mimo"
    - "secure massive iot using hierarchical fast blind deconvolution"
    - "performance of hierarchical sparse detectors for massive mtc"
    - "hierarchical sparse channel estimation for massive mimo"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "reliable recovery of hierarchically sparse signals and application in machine type communications"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "compressive random access using a common overloaded control channel"

Connected through hyperedge HE-8 (induced by source paper "massive connectivity with massive mimo part i device activity detection and channel estimation"):
  with members:
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "a random access protocol for pilot allocation in crowded massive mimo systems"
    - "capacity of gaussian many access channels"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "compressive random access using a common overloaded control channel"
    - "massive mimo for next generation wireless systems"
    - "efficient high dimensional inference in the multiple measurement vector problem"

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)