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: "the price of certainty waterslide curves and the gap to capacity"
  Abstract: The classical problem of reliable point-to-point digital communication is to achieve a low probability of error while keeping the rate high and the total power consumption small. Traditional information-theoretic analysis uses `waterfall' curves to convey the revolutionary idea that unboundedly low probabilities of bit-error are attainable using only finite transmit power. However, practitioners have long observed that the decoder complexity, and hence the total power consumption, goes up when attempting to use sophisticated codes that operate close to the waterfall curve. #R##N#This paper gives an explicit model for power consumption at an idealized decoder that allows for extreme parallelism in implementation. The decoder architecture is in the spirit of message passing and iterative decoding for sparse-graph codes. Generalized sphere-packing arguments are used to derive lower bounds on the decoding power needed for any possible code given only the gap from the Shannon limit and the desired probability of error. As the gap goes to zero, the energy per bit spent in decoding is shown to go to infinity. This suggests that to optimize total power, the transmitter should operate at a power that is strictly above the minimum demanded by the Shannon capacity. #R##N#The lower bound is plotted to show an unavoidable tradeoff between the average bit-error probability and the total power used in transmission and decoding. In the spirit of conventional waterfall curves, we call these `waterslide' curves.

Connected through hyperedge HE-1 (induced by source paper "adaptivity provably helps information theoretic limits on l_0 cost of non adaptive sensing"):
  with members:
    - "info greedy sequential adaptive compressed sensing"
    - "information theoretic bounds for adaptive sparse recovery"
    - "near optimal adaptive compressed sensing"
    - "compressive binary search"
    - "on the limits of sequential testing in high dimensions"
    - "lower bounds for sparse recovery"
    - "information theoretic bounds for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "information capacity of an energy harvesting sensor node"):
  with members:
    - "energy management policies for energy neutral source channel coding"
    - "awgn channel under time varying amplitude constraints with causal information at the transmitter"
    - "towards a communication theoretic understanding of system level power consumption"
    - "information capacity of energy harvesting sensor nodes"
    - "basic performance limits and tradeoffs in energy harvesting sensor nodes with finite data and energy storage"
    - "information theoretic analysis of an energy harvesting communication system"
    - "efficient energy management policies for networks with energy harvesting sensor nodes"
    - "information theory vs queueing theory for resource allocation in multiple access channels"

Connected through hyperedge HE-3 (induced by source paper "information capacity of energy harvesting sensor nodes"):
  with members:
    - "information theoretic analysis of an energy harvesting communication system"
    - "optimal energy management policies for energy harvesting sensor nodes"
    - "efficient energy management policies for networks with energy harvesting sensor nodes"

Connected through hyperedge HE-4 (induced by source paper "the finite dimensional witsenhausen counterexample"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "information embedding meets distributed control"
    - "lattice strategies for the dirty multiple access channel"
    - "a deterministic approach to wireless relay networks"
    - "gaussian interference channel capacity to within one bit"
    - "why block length and delay behave differently if feedback is present"
    - "multiaccess channels with state known to some encoders and independent messages"
    - "cognitive radio an information theoretic perspective"

Connected through hyperedge HE-5 (induced by source paper "performance of ldpc codes under faulty iterative decoding"):
  with members:
    - "loopy belief propagation as a basis for communication in sensor networks"
    - "channel coding the road to channel capacity"
    - "the extraction and complexity limits of graphical models for linear codes"
    - "tight bounds for ldpc and ldgm codes under map decoding"
    - "capacity achieving ensembles for the binary erasure channel with bounded complexity"
    - "density evolution thresholds and the stability condition for non binary ldpc codes"

Connected through hyperedge HE-6 (induced by source paper "green codes energy efficient short range communication"):
  with members:
    - "a channel that heats up"

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)