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: "deepcamp deep convolutional action attribute mid level patterns"
  Abstract: The recognition of human actions and the determination of human attributes are two tasks that call for fine-grained classification. Indeed, often rather small and inconspicuous objects and features have to be detected to tell their classes apart. In order to deal with this challenge, we propose a novel convolutional neural network that mines mid-level image patches that are sufficiently dedicated to resolve the corresponding subtleties. In particular, we train a newly de- signed CNN (DeepPattern) that learns discriminative patch groups. There are two innovative aspects to this. On the one hand we pay attention to contextual information in an origi- nal fashion. On the other hand, we let an iteration of feature learning and patch clustering purify the set of dedicated patches that we use. We validate our method for action clas- sification on two challenging datasets: PASCAL VOC 2012 Action and Stanford 40 Actions, and for attribute recogni- tion we use the Berkeley Attributes of People dataset. Our discriminative mid-level mining CNN obtains state-of-the- art results on these datasets, without a need for annotations about parts and poses.

Connected through hyperedge HE-1 (induced by source paper "pedestrian attribute recognition a survey"):
  with members:
    - "the deeper the better analysis of person attributes recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "improving person re identification by attribute and identity learning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rgb t object tracking benchmark and baseline"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-2 (induced by source paper "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "harmonious attention network for person re identification"
    - "deeply learned part aligned representations for person re identification"
    - "deep mutual learning"
    - "camera style adaptation for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "person re identification by deep joint learning of multi loss classification"

Connected through hyperedge HE-3 (induced by source paper "the devil is in the middle exploiting mid level representations for cross domain instance matching"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "network dissection quantifying interpretability of deep visual representations"
    - "in defense of the triplet loss for person re identification"
    - "deeply learned part aligned representations for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "person re identification by deep joint learning of multi loss classification"
    - "divide and fuse a re ranking approach for person re identification"
    - "hydraplus net attentive deep features for pedestrian analysis"

Connected through hyperedge HE-4 (induced by source paper "encouraging lstms to anticipate actions very early"):
  with members:
    - "rank pooling for action recognition"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "real time action recognition with enhanced motion vector cnns"
    - "built in foreground background prior for weakly supervised semantic segmentation"
    - "actionness estimation using hybrid fully convolutional networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "online localization and prediction of actions and interactions"

Connected through hyperedge HE-5 (induced by source paper "deep action and context aware sequence learning for activity recognition and anticipation"):
  with members:
    - "rank pooling for action recognition"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "real time action recognition with enhanced motion vector cnns"
    - "built in foreground background prior for weakly supervised semantic segmentation"
    - "deep learning for detecting multiple space time action tubes in videos"
    - "actionness estimation using hybrid fully convolutional networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-6 (induced by source paper "mining mid level visual patterns with deep cnn activations"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "cnn rnn a unified framework for multi label image classification"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "modeling visual compatibility through hierarchical mid level elements"
    - "mining discriminative triplets of patches for fine grained classification"
    - "inverting visual representations with convolutional networks"
    - "deep filter banks for texture recognition description and segmentation"

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.CV(Computer Vision and Pattern Recognition)