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: "lossy compression for compute and forward in limited backhaul uplink multicell processing"
  Abstract: We study the transmission over a cloud radio access network in which multiple base stations (BS) are connected to a central processor (CP) via finite-capacity backhaul links. We propose two lattice-based coding schemes. In the first scheme, the base stations decode linear combinations of the transmitted messages, in the spirit of compute-and-forward (CoF), but differs from it essentially in that the decoded equations are remapped to linear combinations of the channel input symbols, sent compressed in a lossy manner to the central processor, and are not required to be linearly independent. Also, by opposition to the standard CoF, an appropriate multi-user decoder is utilized to recover the sent messages. The second coding scheme generalizes the first one by also allowing, at each relay node, a joint compression of the decoded equation and the received signal. Both schemes apply in general, but are more suited for situations in which there are more users than base stations. We show that both schemes can outperform standard CoF and successive Wyner-Ziv schemes in certain regimes, and illustrate the gains through some numerical examples.

Connected through hyperedge HE-1 (induced by source paper "storage space allocation strategy for digital data with message importance"):
  with members:
    - "matching users preference under target revenue constraints in data recommendation systems"
    - "importance of small probability events in big data information measures applications and challenges"
    - "pareto optimal data compression for binary classification tasks"
    - "learning convolutional networks for content weighted image compression"
    - "message importance measure and its application to minority subset detection in big data"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-2 (induced by source paper "importance of small probability events in big data information measures applications and challenges"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "matching users preference under target revenue constraints in data recommendation systems"
    - "differential message importance measure a new approach to the required sampling number in big data structure characterization"
    - "decorrelation of neutral vector variables theory and applications"
    - "group cooperation with optimal resource allocation in wireless powered communication networks"
    - "amplifying inter message distance on information divergence measures in big data"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "linear complexity exponentially consistent tests for universal outlying sequence detection"

Connected through hyperedge HE-3 (induced by source paper "on the capacity regions of cloud radio access networks with limited orthogonal fronthaul"):
  with members:
    - "integer forcing architectures for uplink cloud radio access networks"
    - "generalized compression strategy for the downlink cloud radio access network"
    - "the capacity of the relay channel solution to cover s problem in the gaussian case"
    - "on the capacity of cloud radio access networks with oblivious relaying"
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "an uplink downlink duality for cloud radio access network"
    - "on achievability for downlink cloud radio access networks with base station cooperation"
    - "capacity bounds for diamond networks with an orthogonal broadcast channel"

Connected through hyperedge HE-4 (induced by source paper "on the capacity of cloud radio access networks with oblivious relaying"):
  with members:
    - "distributed information bottleneck method for discrete and gaussian sources"
    - "opening the black box of deep neural networks via information"
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "an uplink downlink duality for cloud radio access network"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "deep learning and the information bottleneck principle"
    - "on discrete alphabets for the two user gaussian interference channel with one receiver lacking knowledge of the interfering codebook"
    - "uplink multicell processing with limited backhaul via per base station successive interference cancellation"

Connected through hyperedge HE-5 (induced by source paper "non parametric message important measure storage code design and transmission planning for big data"):
  with members:
    - "spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis"
    - "message importance measure and its application to minority subset detection in big data"
    - "distributed binary detection with lossy data compression"
    - "wireless communications in the era of big data"
    - "renyi divergence and kullback leibler divergence"
    - "the deterministic time linearity of service provided by fading channels"
    - "identifying the signs of fraudulent accounts using data mining techniques"
    - "on the distortion snr exponent of some layered transmission schemes"

Connected through hyperedge HE-6 (induced by source paper "in network compression for multiterminal cascade mimo systems"):
  with members:
    - "optimized backhaul compression for uplink cloud radio access network"
    - "multihop backhaul compression for the uplink of cloud radio access networks"
    - "distributed function computation over a rooted directed tree"
    - "robust and efficient distributed compression for cloud radio access networks"
    - "compute and forward strategies for cooperative distributed antenna systems"
    - "cascade and triangular source coding with side information at the first two nodes"
    - "cascade multiterminal source coding"

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)