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: "a joint graph based coding scheme for the unsourced random access gaussian channel"
  Abstract: This article introduces a novel communication paradigm for the unsourced, uncoordinated Gaussian multiple access problem. The major components of the envisioned framework are as follows. The encoded bits of every message are partitioned into two groups. The first portion is transmitted using a compressive sensing scheme, whereas the second set of bits is conveyed using a multi-user coding scheme. The compressive sensing portion is key in sidestepping some of the challenges posed by the unsourced aspect of the problem. The information afforded by the compressive sensing is employed to create a sparse random multi-access graph conducive to joint decoding. This construction leverages the lessons learned from traditional IDMA into creating low-complexity schemes for the unsourced setting and its inherent randomness. Under joint message-passing decoding, the proposed scheme offers superior performance compared to existing low-complexity alternatives. Findings are supported by numerical simulations.

Connected through hyperedge HE-1 (induced by source paper "massive unsourced random access based on uncoupled compressive sensing another blessing of massive mimo"):
  with members:
    - "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"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"

Connected through hyperedge HE-2 (induced by source paper "on approximate message passing for unsourced access with coded compressed sensing"):
  with members:
    - "sparse regression codes"
    - "polar coding and random spreading for unsourced multiple access"
    - "an enhanced decoding algorithm for coded compressed sensing"
    - "sparcs for unsourced random access"
    - "a coupled compressive sensing scheme for unsourced multiple access"
    - "techniques for improving the finite length performance of sparse superposition codes"

Connected through hyperedge HE-3 (induced by source paper "massive access for future wireless communication systems"):
  with members:
    - "fundamental limits of many user mac with finite payloads and fading"
    - "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"
    - "on the fundamental limits of mimo massive access communication"
    - "polar coding and random spreading for unsourced multiple access"
    - "energy efficient coded random access for the wireless uplink"
    - "sparcs for unsourced random access"
    - "random access channel coding in the finite blocklength regime"
    - "a new scaling law for activity detection in massive mimo systems"

Connected through hyperedge HE-4 (induced by source paper "energy efficient coded random access for the wireless uplink"):
  with members:
    - "fundamental limits of many user mac with finite payloads and fading"
    - "massive mimo unsourced random access"
    - "massive random access with common alarm messages"
    - "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"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "achievability bounds for t fold irregular repetition slotted aloha scheme in the gaussian mac"

Connected through hyperedge HE-5 (induced by source paper "efficient scheduling for the massive random access gaussian channel"):
  with members:
    - "a coupled compressive sensing scheme for unsourced multiple access"
    - "massive multiple access based on superposition raptor codes for cellular m2m communications"
    - "gaussian multiple access via compute and forward"
    - "massive non orthogonal multiple access for cellular iot potentials and limitations"
    - "capacity of gaussian many access channels"
    - "sign compute resolve for tree splitting random access"
    - "code design for short blocks a survey"
    - "massive machine type communications in 5g physical and mac layer solutions"

Connected through hyperedge HE-6 (induced by source paper "an enhanced decoding algorithm for coded compressed sensing"):
  with members:
    - "sparse regression codes"
    - "massive mimo unsourced random access"
    - "sparcs for unsourced random access"
    - "a coupled compressive sensing scheme for unsourced multiple access"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "polar coding and random spreading for unsourced multiple access"):
  with members:
    - "an enhanced decoding algorithm for coded compressed sensing"
    - "efficient scheduling for the massive random access gaussian channel"
    - "sparcs for unsourced random access"

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)