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: "scalable uplink signal detection in c rans via randomized gaussian message passing"
  Abstract: Cloud Radio Access Network (C-RAN) is a promising architecture for unprecedented capacity enhancement in next-generation wireless networks thanks to the centralization and virtualization of base station processing. However, centralized signal processing in C-RANs involves high computational complexity that quickly becomes unaffordable when the network grows to a huge size. Among the first, this paper endeavours to design a scalable uplink signal detection algorithm, in the sense that both the complexity per unit network area and the total computation time remain constant when the network size grows. To this end, we formulate the signal detection in C-RAN as an inference problem over a bipartite random geometric graph. By passing messages among neighboring nodes, message passing (a.k.a. belief propagation) provides an efficient way to solve the inference problem over a sparse graph. However, the traditional message-passing algorithm is not guaranteed to converge, because the corresponding bipartite random geometric graph is locally dense and contains many short loops. As a major contribution of this paper, we propose a randomized Gaussian message passing (RGMP) algorithm to improve the convergence. Instead of exchanging messages simultaneously or in a fixed order, we propose to exchange messages asynchronously in a random order. The proposed RGMP algorithm demonstrates significantly better convergence performance than conventional message passing. The randomness of the message update schedule also simplifies the analysis, and allows the derivation of the convergence conditions for the RGMP algorithm. In addition, we propose a blockwise RGMP (B-RGMP) algorithm for practical implementation. The average computation time of B-RGMP remains constant when the network size increases.

Connected through hyperedge HE-1 (induced by source paper "cnn based signal detection for banded linear systems"):
  with members:
    - "optimal data detection in large mimo"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "learning to decode linear codes using deep learning"
    - "statistical mechanics of map estimation general replica ansatz"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "deep residual learning for image recognition"
    - "convolutional neural networks for sentence classification"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-2 (induced by source paper "design and analysis of distributed state estimation algorithms based on belief propagation and applications in smart grids"):
  with members:
    - "linear state estimation via 5g c ran cellular networks using gaussian belief propagation"
    - "fast real time dc state estimation in electric power systems using belief propagation"
    - "distributed gauss newton method for ac state estimation a belief propagation approach"
    - "distributed hybrid power state estimation under pmu sampling phase errors"
    - "state estimation in electric power systems using belief propagation an extended dc model"

Connected through hyperedge HE-3 (induced by source paper "machine learning for heterogeneous ultra dense networks with graphical representations"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "a survey on applications of model free strategy learning in cognitive wireless networks"
    - "learning to decode linear codes using deep learning"
    - "large scale distributed semi supervised learning using streaming approximation"

Connected through hyperedge HE-4 (induced by source paper "linear state estimation via 5g c ran cellular networks using gaussian belief propagation"):
  with members:
    - "5g mobile cellular networks enabling distributed state estimation for smart grids"
    - "distributed gauss newton method for ac state estimation using belief propagation"

Connected through hyperedge HE-5 (induced by source paper "centralized and distributed sparsification for low complexity message passing algorithm in c ran architectures"):
  with members:
    - "dynamic nested clustering for parallel phy layer processing in cloud rans"

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.IT(Information Theory)