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: "a survey on application of machine learning techniques in optical networks"
  Abstract: Today, the amount of data that can be retrieved from communications networks is extremely high and diverse (e.g., data regarding users behavior, traffic traces, network alarms, signal quality indicators, etc.). Advanced mathematical tools are required to extract useful information from this large set of network data. In particular, Machine Learning (ML) is regarded as a promising methodological area to perform network-data analysis and enable, e.g., automatized network self-configuration and fault management. In this survey we classify and describe relevant studies dealing with the applications of ML to optical communications and networking. Optical networks and system are facing an unprecedented growth in terms of complexity due to the introduction of a huge number of adjustable parameters (such as routing configurations, modulation format, symbol rate, coding schemes, etc.), mainly due to the adoption of, among the others, coherent transmission/reception technology, advanced digital signal processing and to the presence of nonlinear effects in optical fiber systems. Although a good number of research papers have appeared in the last years, the application of ML to optical networks is still in its early stage. In this survey we provide an introductory reference for researchers and practitioners interested in this field. To stimulate further work in this area, we conclude the paper proposing new possible research directions.

Connected through hyperedge HE-1 (induced by source paper "a very brief introduction to machine learning with applications to communication systems"):
  with members:
    - "proactive received power prediction using machine learning and depth images for mmwave networks"
    - "end to end deep learning of optical fiber communications"
    - "deepcode feedback codes via deep learning"
    - "machine learning at the edge a data driven architecture with applications to 5g cellular networks"
    - "online label recovery for deep learning based communication through error correcting codes"
    - "deep joint source channel coding for wireless image transmission"
    - "deep learning in physical layer communications"
    - "deep learning methods for improved decoding of linear codes"

Connected through hyperedge HE-2 (induced by source paper "geometric constellation shaping for fiber optic communication systems via end to end learning"):
  with members:
    - "experimental demonstration of geometrically shaped constellations tailored to the nonlinear fibre channel"
    - "deep learning of the nonlinear schr odinger equation in fiber optic communications"
    - "end to end learning of communications systems without a channel model"
    - "deep learning of geometric constellation shaping including fiber nonlinearities"
    - "an introduction to deep learning for the physical layer"
    - "applying neural networks in optical communication systems possible pitfalls"
    - "impact of 4d channel distribution on the achievable rates in coherent optical communication experiments"
    - "constellation shaping for wdm systems using 256qam 1024qam with probabilistic optimization"

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)