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: "bit level optimized neural network for multi antenna channel quantization"
  Abstract: Quantized channel state information (CSI) plays a critical role in precoding design which helps reap the merits of multiple-input multiple-output (MIMO) technology. In order to reduce the overhead of CSI feedback, we propose a deep learning based CSI quantization method by developing a joint convolutional residual network (JCResNet) which benefits MIMO channel feature extraction and recovery from the perspective of bit-level quantization performance. Experiments show that our proposed method substantially improves the performance.

Connected through hyperedge HE-1 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo"):
  with members:
    - "adversarial attack on dl based massive mimo csi feedback"
    - "20 years of evolution from cognitive to intelligent communications"
    - "cnn based analog csi feedback in fdd mimo ofdm systems"
    - "deep learning based limited feedback designs for mimo systems"
    - "an efficient deep learning framework for low rate massive mimo csi reporting"
    - "compression and acceleration of neural networks for communications"
    - "overcoming the channel estimation barrier in massive mimo communication systems"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"

Connected through hyperedge HE-2 (induced by source paper "resource allocation using gradient boosting aided deep q network for iot in c rans"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "an introduction to deep reinforcement learning"
    - "mimo channel information feedback using deep recurrent network"
    - "deep forest"
    - "model driven deep learning for physical layer communications"
    - "deep forest towards an alternative to deep neural networks"
    - "xgboost a scalable tree boosting system"
    - "user association in 5g networks a survey and an outlook"

Connected through hyperedge HE-3 (induced by source paper "an efficient deep learning framework for low rate massive mimo csi reporting"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "rethinking floating point for deep learning"
    - "mimo channel information feedback using deep recurrent network"
    - "training deep neural networks with 8 bit floating point numbers"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "deep learning for massive mimo csi feedback"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

Connected through hyperedge HE-4 (induced by source paper "cnn based analog csi feedback in fdd mimo ofdm systems"):
  with members:
    - "deep convolutional compression for massive mimo csi feedback"
    - "machine learning in the air"
    - "mimo channel information feedback using deep recurrent network"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "deep learning for massive mimo csi feedback"
    - "mimo broadcast channels with finite rate feedback"

Connected through hyperedge HE-5 (induced by source paper "a novel cross entropy approach for offloading learning in mobile edge computing"):
  with members:
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "mimo channel information feedback using deep recurrent network"
    - "delay optimal computation task scheduling for mobile edge computing systems"

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)