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: "optimizing affinity based binary hashing using auxiliary coordinates"
  Abstract: In supervised binary hashing, one wants to learn a function that maps a high-dimensional feature vector to a vector of binary codes, for application to fast image retrieval. This typically results in a difficult optimization problem, nonconvex and nonsmooth, because of the discrete variables involved. Much work has simply relaxed the problem during training, solving a continuous optimization, and truncating the codes a posteriori. This gives reasonable results but is quite suboptimal. Recent work has tried to optimize the objective directly over the binary codes and achieved better results, but the hash function was still learned a posteriori, which remains suboptimal. We propose a general framework for learning hash functions using affinity-based loss functions that uses auxiliary coordinates. This closes the loop and optimizes jointly over the hash functions and the binary codes so that they gradually match each other. The resulting algorithm can be seen as a corrected, iterated version of the procedure of optimizing first over the codes and then learning the hash function. Compared to this, our optimization is guaranteed to obtain better hash functions while being not much slower, as demonstrated experimentally in various supervised datasets. In addition, our framework facilitates the design of optimization algorithms for arbitrary types of loss and hash functions.

Connected through hyperedge HE-1 (induced by source paper "push for quantization deep fisher hashing"):
  with members:
    - "fast supervised discrete hashing"
    - "deep supervised discrete hashing"
    - "hashnet deep learning to hash by continuation"
    - "deep supervised hashing with triplet labels"
    - "deep cross modal hashing"
    - "efficient training of very deep neural networks for supervised hashing"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "on the evaluation metric for hashing"):
  with members:
    - "deep supervised discrete hashing"
    - "stochastic generative hashing"
    - "hashnet deep learning to hash by continuation"
    - "how should we evaluate supervised hashing"
    - "learning hash functions using column generation"
    - "self taught hashing for fast similarity search"

Connected through hyperedge HE-3 (induced by source paper "deep discrete supervised hashing"):
  with members:
    - "global hashing system for fast image search"
    - "deep supervised discrete hashing"
    - "fast training of triplet based deep binary embedding networks"
    - "a survey on learning to hash"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "learning to hash for indexing big data a survey"
    - "feature learning based deep supervised hashing with pairwise labels"

Connected through hyperedge HE-4 (induced by source paper "parmac distributed optimisation of nested functions with application to binary autoencoders"):
  with members:
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "learning hashing with affinity based loss functions using auxiliary coordinates"
    - "hashing with binary autoencoders"
    - "deep learning with limited numerical precision"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "distributed optimization of deeply nested systems"
    - "searching in one billion vectors re rank with source coding"

Connected through hyperedge HE-5 (induced by source paper "streaming binary sketching based on subspace tracking and diagonal uniformization"):
  with members:
    - "structured adaptive and random spinners for fast machine learning computations"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "learning to hash for indexing big data a survey"
    - "practical and optimal lsh for angular distance"
    - "compressing neural networks with the hashing trick"

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.LG(Machine Learning)