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: "neighbor discovery for wireless networks via compressed sensing"
  Abstract: This paper studies the problem of neighbor discovery in wireless networks, namely, each node wishes to discover and identify the network interface addresses (NIAs) of those nodes within a single hop. A novel paradigm, called compressed neighbor discovery is proposed, which enables all nodes to simultaneously discover their respective neighborhoods with a single frame of transmission, which is typically of a few thousand symbol epochs. The key technique is to assign each node a unique on-off signature and let all nodes simultaneously transmit their signatures. Despite that the radios are half-duplex, each node observes a superposition of its neighbors' signatures (partially) through its own off-slots. To identify its neighbors out of a large network address space, each node solves a compressed sensing (or sparse recovery) problem. #R##N#Two practical schemes are studied. The first employs random on-off signatures, and each node discovers its neighbors using a noncoherent detection algorithm based on group testing. The second scheme uses on-off signatures based on a deterministic second-order Reed-Muller code, and applies a chirp decoding algorithm. The second scheme needs much lower signal-to-noise ratio (SNR) to achieve the same error performance. The complexity of the chirp decoding algorithm is sub-linear, so that it is in principle scalable to networks with billions of nodes with 48-bit IEEE 802.11 MAC addresses. The compressed neighbor discovery schemes are much more efficient than conventional random-access discovery, where nodes have to retransmit over many frames with random delays to be successfully discovered.

Connected through hyperedge HE-1 (induced by source paper "sparse activity detection for massive connectivity"):
  with members:
    - "capacity of gaussian many access channels"
    - "compressive random access using a common overloaded control channel"
    - "sparse signal processing concepts for efficient 5g system design"
    - "what will 5g be"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "belief propagation for joint sparse recovery"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"

Connected through hyperedge HE-2 (induced by source paper "reed muller sequences for 5g grant free massive access"):
  with members:
    - "towards massive ultra reliable and low latency wireless the art of sending short packets"
    - "what will 5g be"
    - "reed muller sensing matrices and the lasso"

Connected through hyperedge HE-3 (induced by source paper "sparse ofdm a compressive sensing approach to asynchronous neighbor discovery"):
  with members:
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "on frame asynchronous coded slotted aloha asymptotic finite length and delay analysis"
    - "detection and combining techniques for asynchronous random access with time diversity"
    - "coded slotted aloha a graph based method for uncoordinated multiple access"
    - "probabilistic rateless multiple access for machine to machine communication"
    - "many access channels the gaussian case with random user activities"
    - "asynchronous code division random access using convex optimization"
    - "neighbor discovery in a wireless sensor network multipacket reception capability and physical layer signal processing"

Connected through hyperedge HE-4 (induced by source paper "capacity of gaussian many access channels"):
  with members:
    - "on or many access channels"
    - "sparse ofdm a compressive sensing approach to asynchronous neighbor discovery"
    - "identifying randomly activated users via sign compute resolve on graphs"
    - "coded slotted aloha a graph based method for uncoordinated multiple access"
    - "many access for small packets based on precoding and sparsity aware recovery"
    - "many access channels the gaussian case with random user activities"
    - "many broadcast channels definition and capacity in the degraded case"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"

Connected through hyperedge HE-5 (induced by source paper "throughput analysis for full duplex wireless networks with imperfect self interference cancellation"):
  with members:
    - "full duplex mimo small cell networks performance analysis"
    - "outage analysis of full duplex architectures in cellular networks"
    - "asymptotic deployment gain a simple approach to characterize the sinr distribution in general cellular networks"
    - "the mean interference to signal ratio and its key role in cellular and amorphous networks"
    - "throughput analysis for wireless networks with full duplex radios"
    - "low complexity end to end performance optimization in mimo full duplex relay systems"
    - "hybrid full half duplex system analysis in heterogeneous wireless networks"
    - "pushing the limits of full duplex design and real time implementation"

Connected through hyperedge HE-6 (induced by source paper "subspace selection for projection maximization with matroid constraints"):
  with members:
    - "string submodular functions with curvature constraints"

Connected through hyperedge HE-7 (induced by source paper "cooperative slotted aloha for multi base station systems"):
  with members:
    - "coded slotted aloha a graph based method for uncoordinated multiple access"
    - "slotted aloha for networked base stations with spatial and temporal diversity"
    - "slotted aloha for networked base stations"
    - "coded random access how coding theory helps to build random access protocols"
    - "exploiting capture effect in frameless aloha for massive wireless random access"
    - "characterization of coded random access with compressive sensing based multi user detection"
    - "the throughput of slotted aloha with diversity"
    - "aloha random access that operates as a rateless code"

Connected through hyperedge HE-8 (induced by source paper "throughput analysis for wireless networks with full duplex radios"):
  with members:
    - "pushing the limits of full duplex design and real time implementation"
    - "virtual full duplex wireless communication via rapid on off division duplex"
    - "virtual full duplex wireless broadcasting via compressed sensing"
    - "two way transmission capacity of wireless ad hoc networks"

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.NI(Networking and Internet Architecture)