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: "learning hashing with affinity based loss functions using auxiliary coordinates"
  Abstract: In 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 suboptimal. Recent work has applied alternating optimization to the objective 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 closes the loop and optimizes jointly over the hash functions and the binary codes. 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 and unsupervised datasets. In addition, the framework facilitates the design of optimization algorithms for arbitrary types of loss and hash functions.

Connected through hyperedge HE-1 (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"
    - "hashing with binary autoencoders"
    - "optimizing affinity based binary hashing using auxiliary coordinates"
    - "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-2 (induced by source paper "an ensemble diversity approach to supervised binary hashing"):
  with members:
    - "hashing with binary autoencoders"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "geodesic distance function learning via heat flow on vector fields"
    - "a general two step approach to learning based hashing"
    - "distributed optimization of deeply nested systems"
    - "self taught hashing for fast similarity search"
    - "solving multiclass learning problems via error correcting output 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.LG(Machine Learning)