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 "energy scaling laws for distributed inference in random fusion networks"):
  Abstract: The energy scaling laws of multihop data fusion networks for distributed inference are considered. The fusion network consists of randomly located sensors distributed i.i.d. according to a general spatial distribution in an expanding region. Under Markov random field (MRF) hypotheses, among the class of data-fusion policies which enable optimal statistical inference at the fusion center using all the sensor measurements, the policy with the minimum average energy consumption is bounded below by the average energy of fusion along the minimum spanning tree, and above by a suboptimal policy, referred to as Data Fusion for Markov Random Fields (DFMRF). Scaling laws are derived for the energy consumption of the optimal and suboptimal fusion policies. It is shown that the average asymptotic energy of the DFMRF scheme is strictly finite for a class of MRF models with Euclidean stabilizing dependency graphs.

Includes members:
  Member "detection of gauss markov random fields with nearest neighbor dependency":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "diffusion adaptation strategies for distributed estimation over gaussian markov random fields")
  Member "information energy and density for ad hoc sensor networks over correlated random fields large deviations analysis":
    also connected through other hyperedges:
      (none)
  Member "sensor configuration and activation for field detection in large sensor arrays":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "information energy and density for ad hoc sensor networks over correlated random fields large deviations analysis")

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)