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: "information friction and its implications on minimum energy required for communication"
  Abstract: Just as there are frictional losses associated with moving masses on a surface, what if there were frictional losses associated with moving information on a substrate? Indeed, many modes of communication suffer from such frictional losses. We propose to model these losses as proportional to “bit-meters,”  i.e. , the product of “mass” of information ( i.e. , the number of bits) and the distance of information transport. We use this information-friction model to understand the fundamental energy requirements on encoding and decoding in communication circuitry. First, for communication across a binary input additive white Gaussian noise channel, we arrive at fundamental limits on bit-meters (and thus energy consumption) for decoding implementations that have a predetermined input-independent length of messages. For encoding, we relax the fixed-length assumption and derive bounds for flexible-message-length implementations. Using these lower bounds, we show that the total (transmit + encoding + decoding) energy-per-bit must diverge to infinity as the target error probability is lowered to zero. Furthermore, the closer the communication rate is maintained to the channel capacity (as the target error probability is lowered to zero), the fast required decoding energy diverges to infinity.

Connected through hyperedge HE-1 (induced by source paper "information theory of molecular communication directions and challenges"):
  with members:
    - "shannon capacity of signal transduction for multiple independent receptors"
    - "capacity limits of diffusion based molecular timing channels"
    - "comprehensive reactive receiver modeling for diffusive molecular communication systems reversible binding molecule degradation and finite number of receptors"
    - "type based sign modulation for molecular communication"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "a perspective on future research directions in information theory"
    - "upper bound on the capacity of a cascade of nonlinear and noisy channels"
    - "information rates of ask based molecular communication in fluid media"

Connected through hyperedge HE-2 (induced by source paper "on scaling rules for energy of vlsi polar encoders and decoders"):
  with members:
    - "towards approaching total power capacity transmit and decoding power minimization for ldpc codes"
    - "finite length scaling for polar codes"
    - "energy consumption of vlsi decoders"
    - "a high throughput energy efficient implementation of successive cancellation decoder for polar codes using combinational logic"
    - "polar codes speed of polarization and polynomial gap to capacity"
    - "improved bounds on the finite length scaling of polar codes"
    - "hardware architectures for successive cancellation decoding of polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-3 (induced by source paper "energy latency and reliability tradeoffs in coding circuits"):
  with members:
    - "on the total power capacity of regular ldpc codes with iterative message passing decoders"
    - "information theory of molecular communication directions and challenges"
    - "energy harvesting transmitters that heat up throughput maximization under temperature constraints"
    - "on scaling rules for energy of vlsi polar encoders and decoders"
    - "on the energy complexity of ldpc decoder circuits"
    - "energy efficient decoders for compressive sensing fundamental limits and implementations"
    - "modeling and energy optimization of ldpc decoder circuits with timing violations"
    - "computing linear transformations with unreliable components"

Connected through hyperedge HE-4 (induced by source paper "energy harvesting transmitters that heat up throughput maximization under temperature constraints"):
  with members:
    - "energy harvesting broadband communication systems with processing energy cost"
    - "throughput optimal policies for energy harvesting wireless transmitters with non ideal circuit power"
    - "sum rate optimal power policies for energy harvesting transmitters in an interference channel"
    - "optimal energy allocation for wireless communications with energy harvesting constraints"
    - "a general framework for the optimization of energy harvesting communication systems with battery imperfections"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"
    - "optimum transmission policies for battery limited energy harvesting nodes"
    - "optimal packet scheduling in an energy harvesting communication system"

Connected through hyperedge HE-5 (induced by source paper "computing linear transformations with unreliable components"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "density evolution and functional threshold for the noisy min sum decoder"
    - "density evolution for min sum decoding of ldpc codes under unreliable message storage"
    - "energy consumption of vlsi decoders"
    - "coding with encoding uncertainty"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "on the capacity of noisy computations"

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)