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: "cross layer design of distributed sensing estimation with quality feedback part i optimal schemes"
  Abstract: This two-part paper presents a feedback-based cross-layer framework for distributed sensing and estimation of a dynamic process by a wireless sensor network (WSN). Sensor nodes wirelessly communicate measurements to the fusion center (FC). Cross-layer factors such as packet collisions and the sensing-transmission costs are considered. Each SN adapts its sensing-transmission action based on its own local observation quality and the estimation quality feedback from the FC under cost constraints for each SN. In this second part, low-complexity myopic sensing-transmission policies (MPs) are designed to optimize a trade-off between performance and the cost incurred by each SN. The MP is computed in closed form for a coordinated scheme, whereas an iterative algorithm is presented for a decentralized one, which converges to a local optimum. The MP dictates that, when the estimation quality is poor, only the best SNs activate, otherwise all SNs remain idle to preserve energy. For both schemes, the threshold on the estimation quality below which the SNs remain idle is derived in closed form, and is shown to be independent of the number of channels. It is also proved that a single channel suffices for severely energy constrained WSNs. The proposed MPs are shown to yield near-optimal performance with respect to the optimal policy of Part I, also in this issue, at a fraction of the complexity, thus being more suitable for practical WSN deployments.

Connected through hyperedge HE-1 (induced by source paper "multi scale spectrum sensing in dense multi cell cognitive networks"):
  with members:
    - "a parallel algorithm for generating a random graph with a prescribed degree sequence"
    - "multi scale spectrum sensing in small cell mm wave cognitive wireless networks"
    - "understanding noise and interference regimes in 5g millimeter wave cellular networks"
    - "investigation of prediction accuracy sensitivity and parameter stability of large scale propagation path loss models for 5g wireless communications"
    - "gains of restricted secondary licensing in millimeter wave cellular systems"
    - "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"
    - "exploiting device to device communications in joint scheduling of access and backhaul for mmwave small cells"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"

Connected through hyperedge HE-2 (induced by source paper "multi scale spectrum sensing in 5g cognitive networks"):
  with members:
    - "multi scale spectrum sensing in small cell mm wave cognitive wireless networks"
    - "understanding noise and interference regimes in 5g millimeter wave cellular networks"
    - "gains of restricted secondary licensing in millimeter wave cellular systems"
    - "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"
    - "exploiting device to device communications in joint scheduling of access and backhaul for mmwave small cells"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"
    - "coverage and rate analysis for millimeter wave cellular networks"
    - "what will 5g be"

Connected through hyperedge HE-3 (induced by source paper "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"):
  with members:
    - "time invariant error bounds for modified cs based sparse signal sequence recovery"
    - "active sequential hypothesis testing"
    - "dynamic compressive sensing of time varying signals via approximate message passing"
    - "coalitional games in partition form for joint spectrum sensing and access in cognitive radio networks"
    - "sensor scheduling for energy efficient target tracking in sensor networks"
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "cognitive interference management in retransmission based wireless networks"
    - "distributed compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "optimal adaptive random multiaccess in energy harvesting wireless sensor networks"):
  with members:
    - "dynamic compression transmission for energy harvesting multihop networks with correlated sources"
    - "solarstat modeling photovoltaic sources through stochastic markov processes"
    - "asymptotically optimal power allocation for energy harvesting communication networks"
    - "medium access control protocols for wireless sensor networks with energy harvesting"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"
    - "optimal energy management policies for energy harvesting sensor nodes"
    - "efficient energy management policies for networks with energy harvesting sensor nodes"

Connected through hyperedge HE-5 (induced by source paper "ensemble of distributed learners for online classification of dynamic data streams"):
  with members:
    - "popular ensemble methods an empirical study"

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.SY(Systems and Control)