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: "on the energy complexity of ldpc decoder circuits"
  Abstract: It is shown that in a sequence of randomly generated bipartite configurations with number of left nodes approaching infinity, the probability that a particular configuration in the sequence has a minimum bisection width proportional to the number of vertices in the configuration approaches $1$ so long as a sufficient condition on the node degree distribution is satisfied. This graph theory result implies an almost sure $\Omega\left(n^{2}\right)$ scaling rule for the energy of capacity-approaching LDPC decoder circuits that directly instantiate their Tanner Graphs and are generated according to a uniform configuration model, where $n$ is the block length of the code. For a sequence of circuits that have a full set of check nodes but do not necessarily directly instantiate a Tanner graph, this implies an $\Omega\left(n^{1.5}\right)$ scaling rule. In another theorem, it is shown that all (as opposed to almost all) capacity-approaching LDPC decoding circuits that directly implement their Tanner graphs must have energy that scales as $\Omega\left(n\left(\log n\right)^{2}\right)$. These results further imply scaling rules for the energy of LDPC decoder circuits as a function of gap to capacity.

Connected through hyperedge HE-1 (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"
    - "information friction and its implications on minimum energy required for communication"
    - "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-2 (induced by source paper "on the total power capacity of regular ldpc codes with iterative message passing decoders"):
  with members:
    - "energy consumption of vlsi decoders"
    - "towards a communication theoretic understanding of system level power consumption"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "two bit message passing decoders for ldpc codes over the binary symmetric channel"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes"

Connected through hyperedge HE-3 (induced by source paper "modeling and energy optimization of ldpc decoder circuits with timing violations"):
  with members:
    - "on the total power capacity of regular ldpc codes with iterative message passing decoders"
    - "analysis and design of finite alphabet iterative decoders robust to faulty hardware"
    - "density evolution for min sum decoding of ldpc codes under unreliable message storage"
    - "state dependent statistical timing model for voltage scaled circuits"
    - "energy consumption of vlsi decoders"
    - "performance of ldpc codes under faulty iterative decoding"

Connected through hyperedge HE-4 (induced by source paper "towards approaching total power capacity transmit and decoding power minimization for ldpc codes"):
  with members:
    - "energy consumption of vlsi decoders"
    - "towards a communication theoretic understanding of system level power consumption"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "two bit message passing decoders for ldpc codes over the binary symmetric channel"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc 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)