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: "hardware based projection onto the parity polytope and probability simplex"
  Abstract: This paper is concerned with the adaptation to hardware of methods for Euclidean norm projections onto the parity polytope and probability simplex. We first refine recent efforts to develop efficient methods of projection onto the parity polytope. Our resulting algorithm can be configured to have either average computational complexity O (d) or worst case complexity O (d log d) on a serial processor where d is the dimension of projection space. We show how to adapt our projection routine to hardware. Our projection method uses a sub-routine that involves another Euclidean projection; onto the probability simplex. We therefore explain how to adapt to hardware a well know simplex projection algorithm. The hardware implementations of both projection algorithms achieve area scalings of O(d (log d)2) at a delay of O ((log d)2). Finally, we present numerical results in which we evaluate the fixed-point accuracy and resource scaling of these algorithms when targeting a modern FPGA.

Connected through hyperedge HE-1 (induced by source paper "efficient qp admm decoder for binary ldpc codes and its performance analysis"):
  with members:
    - "minimum polytope based linear programming decoder for ldpc codes via admm approach"
    - "the admm penalized decoder for ldpc codes"
    - "hardware based linear program decoding with the alternating direction method of multipliers"
    - "mathematical programming decoding of binary linear codes theory and algorithms"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
    - "improved linear programming decoding of ldpc codes and bounds on the minimum and fractional distance"
    - "efficient linear programming decoding of hdpc codes"
    - "a separation algorithm for improved lp decoding of linear block codes"

Connected through hyperedge HE-2 (induced by source paper "a reduced complexity projection algorithm for admm based lp decoding"):
  with members:
    - "the admm penalized decoder for ldpc codes"
    - "hardware based linear program decoding with the alternating direction method of multipliers"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
    - "decomposition methods for large scale lp decoding"
    - "adaptive methods for linear programming decoding"

Connected through hyperedge HE-3 (induced by source paper "hardware based linear program decoding with the alternating direction method of multipliers"):
  with members:
    - "1 bit massive mu mimo precoding in vlsi"
    - "lp decodable multipermutation codes"
    - "hierarchical and high girth qc ldpc codes"
    - "error floor approximation for ldpc codes in the awgn channel"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
    - "decomposition methods for large scale lp decoding"
    - "adaptive methods for linear programming decoding"
    - "towards low complexity linear programming decoding"

Connected through hyperedge HE-4 (induced by source paper "hardware based admm lp decoding"):
  with members:
    - "the admm penalized decoder for ldpc codes"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
    - "decomposition methods for large scale lp decoding"
    - "adaptive methods for linear programming decoding"
    - "towards low complexity linear programming decoding"

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)