human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "dynamic compression transmission for energy harvesting multihop networks with correlated sources"):
  Abstract: Energy-harvesting wireless sensor networking is an emerging technology with applications to various fields such as environmental and structural health monitoring. A distinguishing feature of wireless sensors is the need to perform both source coding tasks, such as measurement and compression, and transmission tasks. It is known that the overall energy consumption for source coding is generally comparable to that of transmission, and that a joint design of the two classes of tasks can lead to relevant performance gains. Moreover, the efficiency of source coding in a sensor network can be potentially improved via distributed techniques by leveraging the fact that signals measured by different nodes are correlated. In this paper, a data-gathering protocol for multihop wireless sensor networks with energy-harvesting capabilities is studied whereby the sources measured by the sensors are correlated. Both the energy consumptions of source coding and transmission are modeled, and distributed source coding is assumed. The problem of dynamically and jointly optimizing the source coding and transmission strategies is formulated for time-varying channels and sources. The problem consists in the minimization of a cost function of the distortions in the source reconstructions at the sink under queue stability constraints. By adopting perturbation-based Lyapunov techniques, a close-to-optimal online scheme is proposed that has an explicit and controllable tradeoff between optimality gap and queue sizes. The role of side information available at the sink is also discussed under the assumption that acquiring the side information entails an energy cost.

Includes members:
  Member "sparse regression codes for multi terminal source and channel coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse regression codes")
  Member "worst case source for distributed compression with quadratic distortion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "network compression worst case analysis")
  Member "lossy compression of discrete sources via the viterbi algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "theoretical links between universal and bayesian compressed sensing algorithms")
  Member "optimal energy allocation for wireless communications with energy harvesting constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal energy allocation and task offloading policy for wireless powered mobile edge computing systems")
  Member "a general framework for the optimization of energy harvesting communication systems with battery imperfections":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the energy and data storage management in energy harvesting wireless communications")
  Member "transmission with energy harvesting nodes in fading wireless channels optimal policies":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "closed form analysis of non linear age of information in status updates with an energy harvesting transmitter")
  Member "optimal energy management policies for energy harvesting sensor nodes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "achievable rate of multi antenna wsrns with eh constraint in the presence of a jammer")

Question: Please tell me which class the source paper 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)