human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a mathematical theory of imperfect communication energy efficiency considerations in multi level coding"):
  Abstract: A novel framework is presented for the analysis of multi-level coding that takes into account degrees of freedom attended and ignored by the different levels of analysis. It can be shown that for a multi-level coding system, skipped or incomplete error correction at many levels can save energy and provide equally good results to perfect correction. This is the case for both discrete and continuous cases. This has relevance to approximate computing, and also to deep learning networks, which can readily be construed as multiple levels of inadequate error correction reacting to some input signal, but which are typically considered beyond analysis by traditional information theoretical methods. The finding also has significance in natural systems, e.g. neuronal signaling, vision, and molecular genetics, which can be characterized as relying on multiple layers of inadequate error correction.

Includes members:
  Member "a general framework for adversarial examples with objectives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "advjnd generating adversarial examples with just noticeable difference")
  Member "high dimensional spaces deep learning and adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "characterizing the decision boundary of deep neural networks")
  Member "adversarial spheres":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "utilizing network properties to detect erroneous inputs")
  Member "opening the black box of deep neural networks via information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "infogan interpretable representation learning by information maximizing generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "intriguing properties of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial ranking attack and defense")

Question: Please tell me which class the source paper 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)