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 for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
  Abstract: The next wave of wireless technologies will proliferate in connecting sensors, machines, and robots for myriad new applications, thereby creating the fabric for the Internet of Things (IoT). A generic scenario for IoT connectivity involves a massive number of machine-type connections, but in a typical application, only a small (unknown) subset of devices are active at any given instant; therefore, one of the key challenges of providing massive IoT connectivity is to detect the active devices first and then decode their data with low latency. This article advocates the usage of grant-free, rather than grantbased random access schemes to overcome the challenge of massive IoT access. Several key signal processing techniques that promote the performance of the grant-free strategies are outlined, with a primary focus on advanced compressed sensing techniques and their applications for the efficient detection of active devices. We argue that massive multiple-input, multiple-output (MIMO) is especially well suited for massive IoT connectivity because the device detection error can be driven to zero asymptotically in the limit as the number of antennas at the base station (BS) goes to infinity by using the multiplemeasurement vector (MMV) compressed sensing techniques. This article also provides a perspective on several related important techniques for massive access, such as embedding short messages onto the device-activity detection process and the coded random access.

Connected through hyperedge HE-1 (induced by source paper "sparse optimization for green edge ai inference"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "exploiting computation replication for mobile edge computing a fundamental computation communication tradeoff study"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "mobile edge intelligence and computing for the internet of vehicles"
    - "energy efficient processing and robust wireless cooperative transmission for edge inference"
    - "the roadmap to 6g ai empowered wireless networks"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "data shuffling in wireless distributed computing via low rank optimization"

Connected through hyperedge HE-2 (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-3 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access"):
  with members:
    - "design of non orthogonal beamspace multiple access for cellular internet of things"
    - "multi cell sparse activity detection for massive random access massive mimo versus cooperative mimo"
    - "improved scaling law for activity detection in massive mimo systems"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "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"
    - "compressive sensing techniques for next generation wireless communications"

Connected through hyperedge HE-4 (induced by source paper "phase transition analysis for covariance based massive random access with massive mimo"):
  with members:
    - "a dimension reduction based joint activity detection and channel estimation algorithm for massive access"
    - "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"
    - "massive mimo unsourced random access"
    - "joint active user detection and channel estimation in massive access systems exploiting reed muller sequences"
    - "multi cell sparse activity detection for massive random access massive mimo versus cooperative mimo"
    - "improved scaling law for activity detection in massive mimo systems"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "sparse activity detection for massive connectivity"

Connected through hyperedge HE-5 (induced by source paper "massive unsourced random access based on uncoupled compressive sensing another blessing of massive mimo"):
  with members:
    - "a joint graph based coding scheme for the unsourced random access gaussian channel"
    - "polar coding and random spreading for unsourced multiple access"
    - "massive mimo unsourced random access"
    - "generalized approximate message passing for massive mimo mmwave channel estimation with laplacian prior"
    - "an enhanced decoding algorithm for coded compressed sensing"
    - "sparcs for unsourced random access"
    - "improved scaling law for activity detection in massive mimo systems"
    - "a coupled compressive sensing scheme for unsourced multiple access"

Connected through hyperedge HE-6 (induced by source paper "on throughput of compressive random access for one short message delivery in iot"):
  with members:
    - "massive mimo has unlimited capacity"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "device activity and embedded information bit detection using amp in massive mimo"
    - "random pilot and data access in massive mimo for machine type communications"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "massive mimo for wireless sensing with a coherent multiple access channel"
    - "asynchronous code division random access using convex optimization"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-7 (induced by source paper "jointly sparse signal recovery via deep auto encoder and parallel coordinate descent unrolling"):
  with members:
    - "improved scaling law for activity detection in massive mimo systems"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "deep learning sparse ternary projections for compressed sensing of images"
    - "deep networks for compressed image sensing"
    - "deep learning for massive mimo csi feedback"

Connected through hyperedge HE-8 (induced by source paper "bayesian receiver design for grant free noma with message passing based structured signal estimation"):
  with members:
    - "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff"
    - "sparse activity detection for massive connectivity"
    - "massive non orthogonal multiple access for cellular iot potentials and limitations"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "five disruptive technology directions for 5g"
    - "expectation propagation for approximate bayesian inference"
    - "a generalized mean field algorithm for variational inference in exponential families"

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)