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: "deep joint source channel coding for wireless image transmission"
  Abstract: We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols. We parameterize the encoder and decoder functions by two convolutional neural networks (CNNs), which are trained jointly, and can be considered as an autoencoder with a non-trainable layer in the middle that represents the noisy communication channel. Our results show that the proposed deep JSCC scheme outperforms digital transmission concatenating JPEG or JPEG2000 compression with a capacity achieving channel code at low signal-to-noise ratio (SNR) and channel bandwidth values in the presence of additive white Gaussian noise (AWGN). More strikingly, deep JSCC does not suffer from the ``cliff effect'', and it provides a graceful performance degradation as the channel SNR varies with respect to the SNR value assumed during training. In the case of a slow Rayleigh fading channel, deep JSCC learns noise resilient coded representations and significantly outperforms separation-based digital communication at all SNR and channel bandwidth values.

Connected through hyperedge HE-1 (induced by source paper "deep joint transmission recognition for power constrained iot devices"):
  with members:
    - "bottlenet an end to end approach for feature compression in device edge co inference systems"
    - "deep joint source channel coding for wireless image retrieval"
    - "improving device edge cooperative inference of deep learning via 2 step pruning"
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "deepjscc f deep joint source channel coding of images with feedback"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "end to end optimized image compression"
    - "pruning convolutional neural networks for resource efficient inference"

Connected through hyperedge HE-2 (induced by source paper "design of capacity approaching low density parity check codes using recurrent neural networks"):
  with members:
    - "ai coding learning to construct error correction codes"
    - "deep learning methods for improved decoding of linear codes"
    - "an introduction to deep learning for the physical layer"
    - "imposing hard constraints on deep networks promises and limitations"
    - "on deep learning based channel decoding"
    - "an overview of gradient descent optimization algorithms"
    - "adam a method for stochastic optimization"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-3 (induced by source paper "machine learning in the air"):
  with members:
    - "successive refinement of images with deep joint source channel coding"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "deep learning for distributed optimization applications to wireless resource management"
    - "federated learning over wireless fading channels"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "machine learning for wireless communications in the internet of things a comprehensive survey"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "toward intelligent vehicular networks a machine learning framework"

Connected through hyperedge HE-4 (induced by source paper "data importance aware user scheduling for communication efficient edge machine learning"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "optimizing pipelined computation and communication for latency constrained edge learning"
    - "accelerating dnn training in wireless federated edge learning system"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "towards federated learning at scale system design"
    - "semantic effectiveness filtering and control for post 5g wireless connectivity"
    - "mobile edge intelligence and computing for the internet of vehicles"
    - "active learning solution on distributed edge computing"

Connected through hyperedge HE-5 (induced by source paper "deepjscc f deep joint source channel coding of images with feedback"):
  with members:
    - "successive refinement of images with deep joint source channel coding"
    - "deep learning based polar code design"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "end to end deep learning of optical fiber communications"
    - "deepcode feedback codes via deep learning"
    - "deep learning methods for improved decoding of linear codes"
    - "deep learning for joint source channel coding of text"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-6 (induced by source paper "information centric grant free access for iot fog networks edge vs cloud detection and learning"):
  with members:
    - "start making sense semantic plane filtering and control for post 5g connectivity"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"
    - "non orthogonal multiplexing of ultra reliable and broadband services in fog radio architectures"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "towards massive connectivity support for scalable mmtc communications in 5g networks"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"
    - "a brief introduction to machine learning for engineers"
    - "fog computing based radio access networks issues and challenges"

Connected through hyperedge HE-7 (induced by source paper "an introduction to communication efficient edge machine learning"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "data importance aware user scheduling for communication efficient edge machine learning"
    - "high dimensional stochastic gradient quantization for communication efficient edge learning"
    - "reduced dimension design of mimo over the air computing for data aggregation in clustered iot networks"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "automatic recognition of space time constellations by learning on the grassmann manifold"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "sparse communication for distributed gradient descent"

Connected through hyperedge HE-8 (induced by source paper "low density majority codes and the problem of graceful degradation"):
  with members:
    - "a lower bound on the expected distortion of joint source channel coding"
    - "low complexity concatenated ldpc staircase codes"
    - "comparison of channels criteria for domination by a symmetric channel"
    - "strong data processing inequalities for channels and bayesian networks"
    - "a source channel separation theorem with application to the source broadcast problem"
    - "protograph based raptor like ldpc codes"
    - "intermediate performance of rateless codes"
    - "lossy source encoding via message passing and decimation over generalized codewords of ldgm codes"

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)