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 in a wireless sensor network multipacket reception capability and physical layer signal processing"
  Abstract: In randomly deployed networks, such as sensor networks, an important problem for each node is to discover its neighbor nodes so that the connectivity amongst nodes can be established. In this paper, we consider this problem by incorporating the physical layer parameters in contrast to the most of the previous work which assumed a collision channel. Specifically, the pilot signals that nodes transmit are successfully decoded if the strength of the received signal relative to the interference is sufficiently high. Thus, each node must extract signal parameter information from the superposition of an unknown number of received signals. This problem falls naturally in the purview of random set theory (RST) which generalizes standard probability theory by assigning sets, rather than values, to random outcomes. The contributions in the paper are twofold: First, we introduce the realistic effect of physical layer considerations in the evaluation of the performance of logical discovery algorithms; such an introduction is necessary for the accurate assessment of how an algorithm performs. Secondly, given the double uncertainty of the environment (that is, the lack of knowledge of the number of neighbors along with the lack of knowledge of the individual signal parameters), we adopt the viewpoint of RST and demonstrate its advantage relative to classical matched filter detection method.

Connected through hyperedge HE-1 (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 for wireless networks via compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "topology discovery for linear wireless networks with application to train backbone inauguration"):
  with members:
    - "connectivity statistics of store and forward intervehicle communication"

Connected through hyperedge HE-3 (induced by source paper "on the stability of random multiple access with stochastic energy harvesting"):
  with members:
    - "medium access control protocols for wireless sensor networks with energy harvesting"
    - "optimal utilization of a cognitive shared channel with a rechargeable primary source node"
    - "information theoretic analysis of an energy harvesting communication system"
    - "efficient energy management policies for networks with energy harvesting sensor nodes"
    - "asymptotic stability region of slotted aloha"

Connected through hyperedge HE-4 (induced by source paper "spatial performance analysis and design principles for wireless peer discovery"):
  with members:
    - "downlink rate distribution in heterogeneous cellular networks under generalized cell selection"
    - "random deployment of data collectors for serving randomly located sensors"
    - "downlink performance analysis for a generalized shotgun cellular system"

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)