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 off random access channels a compressed sensing framework"
  Abstract: This paper considers a simple on-off random multiple access channel, where n users communicate simultaneously to a single receiver over m degrees of freedom. Each user transmits with probability lambda, where typically lambda n < m << n, and the receiver must detect which users transmitted. We show that when the codebook has i.i.d. Gaussian entries, detecting which users transmitted is mathematically equivalent to a certain sparsity detection problem considered in compressed sensing. Using recent sparsity results, we derive upper and lower bounds on the capacities of these channels. We show that common sparsity detection algorithms, such as lasso and orthogonal matching pursuit (OMP), can be used as tractable multiuser detection schemes and have significantly better performance than single-user detection. These methods do achieve some near-far resistance but--at high signal-to-noise ratios (SNRs)--may achieve capacities far below optimal maximum likelihood detection. We then present a new algorithm, called sequential OMP, that illustrates that iterative detection combined with power ordering or power shaping can significantly improve the high SNR performance. Sequential OMP is analogous to successive interference cancellation in the classic multiple access channel. Our results thereby provide insight into the roles of power control and multiuser detection on random-access signalling.

Connected through hyperedge HE-1 (induced by source paper "random access in c ran for user activity detection with limited capacity fronthaul"):
  with members:
    - "hybrid approximate message passing"
    - "q ary compressive sensing"
    - "compressive demodulation of mutually interfering signals"
    - "message passing de quantization with applications to compressed sensing"
    - "hybrid approximate message passing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "hybrid approximate message passing with applications to structured sparsity"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-2 (induced by source paper "user activity detection in massive random access compressed sensing vs coded slotted aloha"):
  with members:
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "coded random access applying codes on graphs to design random access protocols"
    - "coded random access how coding theory helps to build random access protocols"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "sparse signal recovery for binary compressed sensing by majority voting neural networks"):
  with members:
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "fast neuromimetic object recognition using fpga outperforms gpu implementations"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-4 (induced by source paper "sparse signal processing concepts for efficient 5g system design"):
  with members:
    - "self calibration and biconvex compressive sensing"
    - "robust iterative interference alignment for cellular networks with limited feedback"
    - "characterization of coded random access with compressive sensing based multi user detection"
    - "the achievable performance of convex demixing"
    - "a stability result for sparse convolutions"
    - "fundamentals of throughput maximization with random arrivals for m2m communications"
    - "five disruptive technology directions for 5g"
    - "the papr problem in ofdm transmission new directions for a long lasting problem"

Connected through hyperedge HE-5 (induced by source paper "many access for small packets based on precoding and sparsity aware recovery"):
  with members:
    - "capacity of gaussian many access channels"
    - "a novel uplink data transmission scheme for small packets in massive mimo system"
    - "many access channels the gaussian case with random user activities"
    - "scma codebook design"
    - "massive mimo for next generation wireless systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "near oracle performance of greedy block sparse estimation techniques from noisy measurements"
    - "structured compressed sensing from theory to applications"

Connected through hyperedge HE-6 (induced by source paper "multiuser detection for random access bandwidth request in wimax"):
  with members:
    - "a scalable hybrid mac protocol for massive m2m networks"

Connected through hyperedge HE-7 (induced by source paper "compressive link acquisition in multiuser communications"):
  with members:
    - "asynchronous code division random access using convex optimization"
    - "gps signal acquisition via compressive multichannel sampling"
    - "reduced dimension multiuser detection"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "dynamic updating for ell_ 1 minimization"
    - "compressed sensing of analog signals in shift invariant spaces"

Connected through hyperedge HE-8 (induced by source paper "applications of compressed sensing in communications networks"):
  with members:
    - "structured compressed sensing from theory to applications"
    - "rank awareness in joint sparse recovery"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from noisy entries"
    - "matrix completion from a few entries"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"
    - "exact matrix completion via convex optimization"

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)