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: "on the fly large scale channel gain estimation for massive antenna array base stations"
  Abstract: We propose a novel scheme for estimating the large-scale gains of the channels between user terminals (UTs) and base stations (BSs) in a cellular system. The scheme leverages TDD operation, uplink (UL) training by means of properly designed non-orthogonal pilot codes, and massive antenna arrays at the BSs. Subject to Q resource elements allocated for UL training and using the new scheme, a BS is able to estimate the large-scale channel gains of K users transmitting UL pilots in its cell and in nearby cells, provided K<=Q^2. Such knowledge of the large-scale channel gains of nearby out-of-cells users can be exploited at the BS to mitigate interference to the out-of-cell users that experience the highest levels of interference from the BS. We investigate the large-scale gain estimation performance provided by a variety of non-orthogonal pilot codebook designs. Our simulations suggest that among all the code designs considered, Grassmannian line-packing type codes yield the best large-scale channel gain estimation performance.

Connected through hyperedge HE-1 (induced by source paper "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"):
  with members:
    - "improved scaling law for activity detection in massive mimo systems"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "on the restricted isometry of the columnwise khatri rao product"
    - "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"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"

Connected through hyperedge HE-2 (induced by source paper "grant free massive random access with a massive mimo receiver"):
  with members:
    - "phase transition analysis for covariance based massive random access with massive mimo"
    - "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"
    - "on the restricted isometry property of centered self khatri rao products"
    - "polar coding and random spreading for unsourced multiple access"
    - "an enhanced decoding algorithm for coded compressed sensing"
    - "improved scaling law for activity detection in massive mimo systems"
    - "on the restricted isometry of the columnwise khatri rao product"
    - "sparse activity detection for massive connectivity"

Connected through hyperedge HE-3 (induced by source paper "massive mimo unsourced random access"):
  with members:
    - "improved scaling law for activity detection in massive mimo systems"
    - "sparse activity detection for massive connectivity"
    - "sparse signal processing for grant free massive iot connectivity"
    - "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"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "long range communications in unlicensed bands the rising stars in the iot and smart city scenarios"
    - "belief propagation for joint sparse recovery"

Connected through hyperedge HE-4 (induced by source paper "sparse non negative recovery from biased subgaussian measurements using nnls"):
  with members:
    - "visible light communications based indoor positioning via compressed sensing"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "learning without concentration"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "compressed sensing performance bounds under poisson noise"

Connected through hyperedge HE-5 (induced by source paper "improved scaling law for activity detection in massive mimo systems"):
  with members:
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "a scalable and statistically robust beam alignment technique for mm wave systems"
    - "user activity detection in massive random access compressed sensing vs coded slotted aloha"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "on the gap between rip properties and sparse recovery conditions"
    - "efficient high dimensional inference in the multiple measurement vector problem"

Connected through hyperedge HE-6 (induced by source paper "on the fly uplink training and pilot code sequence design for cellular networks"):
  with members:
    - "directional training and fast sector based processing schemes for mmwave channels"

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)